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Prediction method for stationary crossflow instability transition in supersonic boundary layers based on multi-layer perceptron 认领 引用 被引量：2
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作者 Lei Qiao Xi Jiang +5 位作者 Jiakun Fan Yutian Wang Lu Xie Ningjuan Dong Jiakuan Xu Junqiang Bai 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期36-51,共16页
Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory ... Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory predictive capabilities for this kind of transition,its practical implementation faces inherent limitations:the requirement of first-and second-order wallnormal derivatives of boundary layer velocityemperature profiles,the need for initial eigenvalue guesses,and the computational burden of solving eigenvalue problems.To address these challenges,this study develops a multi-layer perceptron(MLP)model tailored for linear stability analysis of three-dimensional compressible boundary layers based on the artificially defined quasi-three-dimensional non-similar boundary layer solutions.The boundary layer edge flow parameters and perturbation characteristics are mapped to eigenvalues or local growth rates of the envelop curves through fully connected layers.This architecture eliminates the need for computing wall-normal derivatives of velocityemperature profiles,initial eigenvalue estimation,and direct eigenvalue problem solving.Extensive validation across varying operational conditions and geometries(airfoils and swept wings)demonstrates exceptional agreement between the MLP’s predictions(eigenvalues and disturbance amplification factors)and traditional LST results.Furthermore,the model’s transition prediction capability is rigorously verified using National Aeronautics and Space Administration’s supersonic swept-wing crossflow-dominated transition benchmark,incorporating both stability analysis and flight test data.Results confirm the model is an efficient and reliable computational framework for transition prediction in three-dimensional finite-span wings. 展开更多
关键词 Supersonic swept wing Boundary layer transition Crossflow instability Linear stability theory eN method Multi-layer perceptron
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Intraocular pressure prediction method combining finite element simulations and multi-layer perceptron neural network 认领 引用
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作者 Shi Yan Xiaocheng Hu +2 位作者 Xiaohui Song Ke Yao Shaoxing Qu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第4期1-14,共14页
Intraocular pressure(IOP)is a key parameter to diagnose glaucoma disease and assess the treatment effect of cornea after refractive surgery.Current refractive surgeries inevitably change the configuration of the corne... Intraocular pressure(IOP)is a key parameter to diagnose glaucoma disease and assess the treatment effect of cornea after refractive surgery.Current refractive surgeries inevitably change the configuration of the cornea,making it difficult to measure IOP accurately using conventional methods.The prediction method proposed in this article can accurately measure the IOP after refractive surgery.In this study,firstly,the finite element models of cornea free of IOP depicted by various vertex height,thickness,and radius are established,and the deformation of the cornea under different IOP is predicted.Based on the dataset obtained from the numerical simulations and the multi-layer perceptron neural network algorithm,two prediction models for the vertex height and thickness of the cornea free of IOP and for the configuration under IOP are developed,and a prediction method for IOP is then proposed by combining the two models.Following the similar way,two prediction models respectively for the parameters of the presumptive initial configuration of the cornea to undergo refractive surgery and for those of the cornea after surgery are constructed,and a prediction method for IOP of cornea after the surgery is presented.The validity of the prediction methods for regular IOP and that after refractive surgery is demonstrated using the clinical data from some volunteers.The proposed methods provide an efficient prediction method for regular IOP and that after refractive surgery. 展开更多
关键词 Intraocular pressure Prediction method Refractive surgery Numerical simulations Multi-layer perceptron network
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LOEV-APO-MLP:Latin Hypercube Opposition-Based Elite Variation Artificial Protozoa Optimizer for Multilayer Perceptron Training 认领 引用
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作者 Zhiwei Ye Dingfeng Song +7 位作者 Haitao Xie Jixin Zhang Wen Zhou Mengya Lei Xiao Zheng Jie Sun Jing Zhou Mengxuan Li 《Computers, Materials & Continua》 SCIE EI 2025年第12期5509-5530,共22页
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ... The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods. 展开更多
关键词 Artificial protozoa optimizer multilayer perceptron Latin hypercube sampling opposition-based learning neural network training
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Optimized graph neural network-multilayer perceptron fusion classifier for metastatic prostate cancer detection in Western and Asian populations 认领 引用
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作者 Fengxian Han Xiaohui Fan +12 位作者 Pengwei Long Wenhui Zhang Qiting Li Yingxuan Li Xingpeng Guo Yinran Luo Rongqi Wen Sheng Wang Shan Zhang Yizhuo Li Yan Wang Xu Gao Jing Li 《Asian Journal of Urology》 CAS CSCD 2025年第3期327-337,共11页
Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limite... Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows. 展开更多
关键词 Prostate cancer Machine learning Multilayer perceptron Graph neural network
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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron 认领 引用
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作者 Ahmed A.Ewees Mohammed A.A.Al-Qaness +1 位作者 Ali Alshahrani Mohamed Abd Elaziz 《Computers, Materials & Continua》 SCIE EI 2025年第5期2287-2303,共17页
Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys... Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R2)of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting. 展开更多
关键词 Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search
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Segmentwise Multilayer Perceptrons for Speech Emotion Recognition 认领 引用
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作者 Ziying Zhang Changzheng Liu 《国际计算机前沿大会会议论文集》 2025年第1期203-213,共11页
With the increasing popularity of mobile internet devices,speech emotion recognition has become a convenient and valuable means of human-computer interaction.The performance of speech emotion recognition depends on th... With the increasing popularity of mobile internet devices,speech emotion recognition has become a convenient and valuable means of human-computer interaction.The performance of speech emotion recognition depends on the discriminating and emotion-related utterance-level representations extracted from speech.Moreover,sufficient data are required to model the relationship between emotional states and speech.Mainstream emotion recognition methods cannot avoid the influence of the silence period in speech,and environmental noise significantly affects the recognition performance.This study intends to supplement the silence periods with removed speech information and applies segmentwise multilayer perceptrons to enhance the utterance-level representation aggregation.In addition,improved semisupervised learning is employed to overcome the prob-lem of data scarcity.Particular experiments are conducted to evaluate the proposed method on the IEMOCAP corpus,which reveals that it achieves 68.0%weighted accuracy and 68.8%unweighted accuracy in four emotion classifications.The experimental results demonstrate that the proposed method aggregates utterance-level more effectively and that semisupervised learning enhances the performance of our method. 展开更多
关键词 speech emotion recognition segmentwise multilayer perceptron semisupervised learning emotion classification
Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network 认领 引用 被引量：17
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作者 Bhatawdekar Ramesh Murlidhar Hoang Nguyen +4 位作者 Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第6期1413-1427,共15页
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t... In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R2),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R2 of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. 展开更多
关键词 Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)
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Multilayer perceptron neural network activated by adaptive Gaussian radial basis function and its application to predict lid-driven cavity flow 认领 引用 被引量：7
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作者 Qinghua Jiang Lailai Zhu +1 位作者 Chang Shu Vinothkumar Sekar 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第12期1757-1772,共16页
To improve the performance of multilayer perceptron(MLP)neural networks activated by conventional activation functions,this paper presents a new MLP activated by univariate Gaussian radial basis functions(RBFs)with ad... To improve the performance of multilayer perceptron(MLP)neural networks activated by conventional activation functions,this paper presents a new MLP activated by univariate Gaussian radial basis functions(RBFs)with adaptive centers and widths,which is composed of more than one hidden layer.In the hidden layer of the RBF-activated MLP network(MLPRBF),the outputs of the preceding layer are first linearly transformed and then fed into the univariate Gaussian RBF,which exploits the highly nonlinear property of RBF.Adaptive RBFs might address the issues of saturated outputs,low sensitivity,and vanishing gradients in MLPs activated by other prevailing nonlinear functions.Finally,we apply four MLP networks with the rectified linear unit(ReLU),sigmoid function(sigmoid),hyperbolic tangent function(tanh),and Gaussian RBF as the activation functions to approximate the one-dimensional(1D)sinusoidal function,the analytical solution of viscous Burgers’equation,and the two-dimensional(2D)steady lid-driven cavity flows.Using the same network structure,MLP-RBF generally predicts more accurately and converges faster than the other threeMLPs.MLP-RBF using less hidden layers and/or neurons per layer can yield comparable or even higher approximation accuracy than other MLPs equipped with more layers or neurons. 展开更多
关键词 Multilayer perceptron neural network Activation function Radial basis function Numerical approximation
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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data 认领 引用 被引量：6
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作者 Lun Gao Ran-Hong Xie +2 位作者 Li-Zhi Xiao Shuai Wang Chen-Yu Xu 《Petroleum Science》 SCIE CAS CSCD 2022年第2期570-580,共11页
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca... In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%. 展开更多
关键词 Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning DBSCAN algorithm Multi-layer perceptron
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FPGA implementation of bit-stream neuron and perceptron based on sigma delta modulation 认领 引用 被引量：2
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作者 梁勇 王志功 +1 位作者 孟桥 郭晓丹 《Journal of Southeast University(English Edition)》 EI CAS 2012年第3期282-286,共5页
To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（Σ... To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（ΣΔ） modulation is presented.The bit-stream adder,multiplier,threshold function unit and fully digital ΣΔ modulator are implemented in a field programmable gate array（FPGA）,and these bit-stream arithmetical units are employed to build the bit-stream artificial neuron.The function of the bit-stream artificial neuron is verified through the realization of the logic function and a linear classifier.The bit-stream perceptron based on the bit-stream artificial neuron with the pre-processed structure is proved to have the ability of nonlinear classification.The FPGA resource utilization of the bit-stream artificial neuron shows that the bit-stream ANN hardware implementation method can significantly reduce the demand of the ANN hardware resources. 展开更多
关键词 bit-stream artificial neuron perceptron sigma delta field programmable gate array（FPGA）
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Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable 认领 引用 被引量：1
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作者 Mengqi Wang Y.T.Feng +1 位作者 Shaoheng Guan Tongming Qu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第6期2198-2218,共21页
One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne... One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials. 展开更多
关键词 Granular materials History-dependence Multi-layer perceptron(MLP) Discrete element method FEM-DEM Machine learning
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The development of a knowledge base in an expert system based on the four-layer perceptron neural network 认领 引用 被引量：1
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作者 谈理 刘谨 梅丽婷 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2007年第4期552-556,共5页
Owing to continuous production lines with large amount of consecutive controls, various control signals and huge logistic relations, this paper introduced the methods and principles of the development of knowledge bas... Owing to continuous production lines with large amount of consecutive controls, various control signals and huge logistic relations, this paper introduced the methods and principles of the development of knowledge base in a fault diagnosis expert system that was based on machine learning by the four-layer perceptron neural network. An example was presented. By combining differential function with not differential function and back propagation of error with back propagation of expectation, the four-layer perceptron neural network was established. And it was good for solving such a bottleneck problem in knowledge acquisition in expert system and enhancing real-time on-line diagnosis. A method of synthetic back propagation was designed, which broke the limit to non-differentiable function in BP neural network. 展开更多
关键词 fault diagnosis expert system the four-layer perceptron neural network machine learning
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Reconstructing shock front of unstable detonations based on multi-layer perceptron 认领 引用
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作者 Lin Zhou Honghui Teng +2 位作者 Hoi Dick Ng Pengfei Yang Zonglin Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第11期1610-1623,I0001,共14页
The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonati... The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front,but the corresponding lead shock needs to be reconstructed building the complete flow field.Using the multi-layer perceptron(MLP)approach,we propose a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front.The method is verified through the numerical results of one-and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model.Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture,which influences prominently the cellular detonation instability and hence,the distortion of the lead shock surface.To select the input variables for training and evaluate their influence on the effectiveness of the proposed method,five groups,one with six variables,and the other with four variables,are tested and analyzed in the MLP model.The trained MLP is tested in the cases with different activation energies,demonstrates the inspiring generalization capability.This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves. 展开更多
关键词 Cellular detonation Lead shock evolution Multi-layer perceptron Numerical simulations
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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data 认领 引用
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作者 Uddagiri Sirisha Parvathaneni Naga Srinivasu +4 位作者 Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz 《Computers, Materials & Continua》 SCIE EI 2024年第8期2301-2330,共30页
Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To kn... Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process. 展开更多
关键词 Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering
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Recommendation System Based on Perceptron and Graph Convolution Network 认领 引用
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作者 Zuozheng Lian Yongchao Yin Haizhen Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期3939-3954,共16页
The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combinatio... The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms. 展开更多
关键词 Recommendation system graph convolution network attention mechanism multi-layer perceptron
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Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems 认领 引用
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作者 Mustufa Haider Abidi Hisham Alkhalefah Mohamed K.Aboudaif 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期977-997,共21页
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca... The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%. 展开更多
关键词 Smart healthcare systems multilayer perceptron cybersecurity adversarial attack detection Healthcare 4.0
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Digital modulation classification using multi-layer perceptron and time-frequency features 认领 引用
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作者 Yuan Ye Mei Wenbo 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第2期249-254,共6页
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio... Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier. 展开更多
关键词 Digital modulation classification Time-frequency feature Time-frequency distribution Multi-layer perceptron.
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基于Perceptron的非线性滑模控制 认领 引用
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作者 刘艳明 杨叔子 《信息与控制》 北大核心 1996年第4期193-198,共6页
利用Perceptron的两类模式分类特性对非线性滑动模态方程的Lyapunov函数进行训练，以求得非线性系统的切换函数，并进行滑模控制器设计，为非线性滑模控制系统的综合设计提供了一条新的途径，并应用于车削系统中．
关键词 Perceptron模型 神经网络 非线性 滑模控制
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Active Online Learning in the Binary Perceptron Problem 认领 引用
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作者 Hai-Jun Zhou 《Communications in Theoretical Physics》 SCIE CAS CSCD 2019年第2期243-252,共10页
The binary perceptron is the simplest artificial neural network formed by N input units and one output unit, with the neural states and the synaptic weights all restricted to ±1 values. The task in the teacher-st... The binary perceptron is the simplest artificial neural network formed by N input units and one output unit, with the neural states and the synaptic weights all restricted to ±1 values. The task in the teacher-student scenario is to infer the hidden weight vector by training on a set of labeled patterns. Previous efforts on the passive learning mode have shown that learning from independent random patterns is quite inefficient. Here we consider the active online learning mode in which the student designs every new Ising training pattern. We demonstrate that it is mathematically possible to achieve perfect(error-free) inference using only N designed training patterns, but this is computationally unfeasible for large systems. We then investigate two Bayesian statistical designing protocols, which require 2.3N and 1.9N training patterns, respectively, to achieve error-free inference. If the training patterns are instead designed through deductive reasoning, perfect inference is achieved using N + log_2N samples. The performance gap between Bayesian and deductive designing strategies may be shortened in future work by taking into account the possibility of ergodicity breaking in the version space of the binary perceptron. 展开更多
关键词 neural network perceptron online learning belief propagation statistical inference
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Camera-Radar Fusion Sensing System Based on Multi-Layer Perceptron 认领 引用 被引量：2
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作者 YAO Tong WANG Chunxiang QIAN Yeqiang 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期561-568,共8页
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems... Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles. 展开更多
关键词 intelligent vehicle environmental perception system sensor fusion multi-layer perceptron
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