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Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
1
作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
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A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification 认领 引用
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作者 Xu Chen Shuai Wang Kaixun He 《Computers, Materials & Continua》 SCIE EI 2025年第10期1779-1806,共28页
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in... Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms. 展开更多
关键词 Photovoltaic model parameter identification cooperation search algorithm adaptive multiple learning chaotic grouping reflection
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Application of Improved PSO Algorithm in Hydraulic Pressing System Identification 认领 引用 被引量：1
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作者 YU Yu-zhen REN Xin-yi +1 位作者 DU Feng-shan SHI Jun-jie 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2012年第9期29-35,共7页
In view of characteristics of particle swarm optimization(PSO)algorithm of fast convergence but easily falling into local optimum value,a novel improved particle swarm optimization algorithm is put forward,and it is a... In view of characteristics of particle swarm optimization(PSO)algorithm of fast convergence but easily falling into local optimum value,a novel improved particle swarm optimization algorithm is put forward,and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process.In order to maintain population diversity and enhance global optimization capability,the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity,randomness and sensitivity to initial value.When the improved algorithm is used to identify parameters of hydraulic pressure system,the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation.A new method was provided for hydraulic AGC system model identification. 展开更多
关键词 hydraulic pressing system PSO algorithm chaos self-adaptive parameter identification
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Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm 认领 引用 被引量：1
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作者 Annan Jiang Xinping Guo +1 位作者 Shuai Zheng Mengfei Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第6期1177-1199,共23页
Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint mode... Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel. 展开更多
关键词 Gauss process regression differential evolution algorithm ubiquitous-joint model parameter identification orthogonal design
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Modeling of bolted beam utilizing modified joint element and identification of contact parameters at the joint 认领 引用
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作者 Xiang Li Zhousuo Zhang +1 位作者 Zhuofan Zhou Dian Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期374-382,共9页
Research on the modeling of bolted connection structures primarily centers on the characterization of the connection interface.Accurate equivalent modeling of the connection interface is crucial for the effective mode... Research on the modeling of bolted connection structures primarily centers on the characterization of the connection interface.Accurate equivalent modeling of the connection interface is crucial for the effective modeling of bolted connection structures.This article considers the misalignment between the bolt plane and the beam plane,and establishes a modified joint element representing the bolted connection by using the seriesstiffness method.The contact stiffness in this modified joint element are identified using a genetic algorithm with an “emperor selection” strategy.The bolted connection beam model is achieved by refining the Euler-Bernoulli beam model through the incorporation of a modified joint element.The maximum error between the model calculation results and experimental results for each order of natural frequency is 2.39%.This demonstrates the feasibility of accurately characterizing the bolted connection beam through the utilization of the modified joint element for equivalent modeling.This research proposes a modeling method for bolted connection beams that accounts for the misalignment between the bolt plane and the beam plane,significantly enhancing modeling accuracy. 展开更多
关键词 Bolted joints Modified joint element Genetic algorithm Parameter identification
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Intelligent Identification of Natural Fractures in Tight Sandstone:Optimal Model Coupling in Ensemble Frameworks 认领 引用 被引量：1
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作者 Ma Sheng-lun Zhang Zhao-hui +3 位作者 Zhang Jiao-sheng Liao jian-bo Zhang Wen-ting Zou Jian-dong 《Applied Geophysics》 SCIE CSCD 2026年第1期262-284,432,共23页
The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irr... The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irregular geometries,signicant variations in height,and complex lling materials,leading to intricate conventional logging responses with pronounced multi-solution ambiguities that complicate accurate identication.To address this challenge,this study proposes a multi-model selective coupling identication method.This approach incorporated data cleaning,augmentation,and resampling techniques during the preprocessing phase.Subsequently,multi-dimensional feature extraction and cascade-based feature selection were performed,followed by optimizing model parameters using random search,Bayesian optimization,and grid search algorithms.High-performing models were selected via an evaluation framework.These models were then coupled through voting mechanisms to construct a robust identication model capable of deeply exploring the nonlinear relationship between fractures and logging data.The proposed method achieved an 85.19%fracture identication accuracy in blind tests involving 27 fracture segments across three wells,demonstrating strong identication capability.This methodology provides a valuable reference for fracture identication in hydrocarbon reservoirs within the Hongde area. 展开更多
关键词 Algorithm models Data processing Selective coupling Fracture identification
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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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A novel integrated framework for enhanced water source identification 认领 引用
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作者 CHAI Xin MA Xiaomin +4 位作者 LI Han WU Baobao LIU Junsheng GUAN Haipeng YANG Zhenwei 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1318-1333,共16页
Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address t... Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address this challenge,this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique(SMOTE),Zebra Optimization Algorithm(ZOA),and Light Gradient Boosting Machine(LightGBM).Initially,SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset,thereby generating a balanced water sample dataset and mitigating class distribution disparities.Subsequently,an efficient water source identification model is constructed by combining ZOA with LightGBM,leveraging the strengths of both algorithms.The model’s performance is validated using a test set and compared with other common classification models.Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples.ZOA parameter tuning accelerates model convergence and further improves classification accuracy,optimizing the model’s overall performance.In experimental validation,the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41%and a F1 score of 88.24%,outperforming six other classification models.The method proposed in this paper can accurately identify water source types,effectively addressing the issue of low classification accuracy caused by imbalanced water sample data.It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines. 展开更多
关键词 Water source identification Machine learning Synthetic minority over-sampling technique Zebra Optimization Algorithm Isolation Forest
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Identification of H2 and NH3 gases using calorimetric signals and transient response through machine learning 认领 引用
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作者 Wenxin Luo Yingcong Zheng +1 位作者 Yijun Liu Mingjie Li 《Journal of Semiconductors》 EI CAS CSCD 2026年第2期52-59,共8页
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO2 sensor to simultaneously... Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO2 sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H2 and NH3 gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays. 展开更多
关键词 MOS sensor gas identification MEMS technology algorithm analysis
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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm 认领 引用 被引量：11
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作者 Tie Yan Rui Xu +2 位作者 Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1135-1148,共14页
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ... Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation. 展开更多
关键词 Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction
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Application of Genetic Algorithms in Identification ofLinear Time-Varying System 认领 引用 被引量：4
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作者 Zhichun Mu KeLiu +4 位作者 Zichao Wang Datai Yu D. Koshal D. Pearce Information Engineering School, University of Science & Technology Beijing, Beijing 100083, China School of Engineering, University of Brighton, Brighton, UK 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS 2000年第1期58-62,共5页
By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identific... By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method. 展开更多
关键词 genetic algorithm, system identification linear system
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Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine 认领 引用 被引量：3
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作者 Linguo Li Lijuan Sun +2 位作者 Jian Guo Shujing Li Ping Jiang 《Computers, Materials & Continua》 SCIE EI 2020年第10期761-775,共15页
As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new meth... As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases. 展开更多
关键词 Crops disease identification extreme learning machine improved genetic algorithm
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Intelligent risk identification of gas drilling based on nonlinear classification network 认领 引用 被引量：3
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作者 Wen-He Xia Zong-Xu Zhao +4 位作者 Cheng-Xiao Li Gao Li Yong-Jie Li Xing Ding Xiang-Dong Chen 《Petroleum Science》 SCIE EI CAS CSCD 2023年第5期3074-3084,共11页
During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent ... During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development. 展开更多
关键词 Gas drilling Intelligent identification of drilling risk Nonlinear classification RBF Neural Network K-means algorithm
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An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification 认领 引用 被引量：7
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作者 Bao-Chang Xu Ying-Ying Zhang 《International Journal of Automation and computing》 CSCD 2014年第4期434-440,共7页
Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to impr... Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation system.Compared with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance. 展开更多
关键词 Gravitational search algorithm orbital change optimization neural network system identification
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Unmanned wave glider heading model identification and control by artificial fish swarm algorithm 认领 引用 被引量：3
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作者 WANG Lei-feng LIAO Yu-lei +2 位作者 LI Ye ZHANG Wei-xin PAN Kai-wen 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第9期2131-2142,共12页
We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th... We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified. 展开更多
关键词 unmanned wave glider artificial fish swarm algorithm heading model parameters identification control parameters optimization
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Parameter Identification of Magic Formula Tire Model Based on Fibonacci Tree Optimization Algorithm 认领 引用 被引量：4
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作者 FENG Shilin ZHAO Youqun +2 位作者 DENG Huifan WANG Qiuwei CHEN Tingting 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期647-657,共11页
The magic formula(MF)tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoi... The magic formula(MF)tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum,a parameter identification method based on the Fibonacci tree optimization(FTO)algorithm is proposed,which is used to identify the parameters of the MF tire model.The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly.The global search rule in the original FTO was modified to improve its efficiency.The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values.In addition,it had a better global optimization performance than genetic algorithm(GA)and particle swarm optimization(PSO).The root mean square error values optimized with FTO were 5.09%,10.22%,and 3.98%less than the GA,and 6.04%,4.47%,and 16.42%less than the PSO in pure lateral and longitudinal forces,and pure aligning torque parameter identification.The parameter identification method based on FTO was found to be effective. 展开更多
关键词 magic formula tire model parameter identification Fibonacci tree optimization(FTO)algorithm
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A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm 认领 引用 被引量：13
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作者 Xusheng Lei Yuhu Du 《Journal of Bionic Engineering》 SCIE EI 2010年第2期142-149,共8页
This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the ... This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the data sequences of flighttests as inputs(control signals for servos)and outputs(aircraft’s attitude and velocity information).After data preprocessing,thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model,the small unmanned aerial rotorcraft can perform hovering,turning,and straight flight tasks in real flight tests. 展开更多
关键词 small unmanned aerial rotorcraft dynamic space model model identification adaptive genetic algorithm
Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm 认领 引用 被引量：2
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作者 Yan Shi Jiange Kou +2 位作者 Zhenlei Chen Yixuan Wang Qing Guo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第2期100-114,共15页
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i... Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value. 展开更多
关键词 Parameter identification Enhanced whale optimization algorithm(EWOA) Backstepping Human-robot interaction Lower limb exoskeleton
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Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography 认领 引用 被引量：3
20
作者 LIU Lei YANG Peng +1 位作者 LIU Zuojun SONG Yinmao 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第1期84-92,共9页
The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode.Surface electromyography(sEMG)combined with high-order zero-crossing was used to identify the prosthetic leg locomotio... The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode.Surface electromyography(sEMG)combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes.sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification.High-order zero-crossing were computed as the sEMG features regarding locomotion modes.Relevance vector machine(RVM)classifier was investigated.Bat algorithm(BA)was used to compute the RVM classifier kernel function parameters.The classification performance of the particle swarm optimization-relevance vector machine(PSO-RVM)and RVM classifiers was compared.The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes:upslope,downgrade,level-ground walking and stair ascent/descent. 展开更多
关键词 intelligent prosthesis surface electromyography(sEMG) relevance vector machine(RVM) high-order zero-crossing bat algorithm(BA) locomotion-mode identification
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