Effects of the chaotic noise on the performance of a neural network model for optimization problems

1995 ◽  
Vol 51 (4) ◽  
pp. R2693-R2696 ◽  
Author(s):  
Yoshinori Hayakawa ◽  
Atsushi Marumoto ◽  
Yasuji Sawada
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Problems

scholarly journals A Nonpenalty Neurodynamic Model for Complex-Variable Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bao Liu ◽  
Xuehui Mei ◽  
Haijun Jiang ◽  
Lijun Wu
Keyword(s):  
Neural Network ◽  
Differential Inclusion ◽  
Network Model ◽  
Neural Network Model ◽  
Complex Variable ◽  
Optimization Model ◽  
Optimization Problems ◽  
Numerical Examples ◽  
Practical Applications ◽  
Proposed Model

In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.

scholarly journals Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Rui Yang ◽  
Zenghui An ◽  
Shijun Song
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Recognition ◽  
Optimization Problems ◽  
Self Regulation ◽  
Training Model ◽  
Feedback Mechanism ◽  
Model Parameters

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.

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