Multi-resolution Wavelet Neural Network Learning Algorithm Based on Artificial Fish Swarm Algorithm

2021 ◽  
Author(s):  
Yuan He ◽  
Xiaowei Zhao ◽  
Runze Guo ◽  
Xusheng Gan
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Neural Network Learning ◽  
Artificial Fish Swarm Algorithm ◽  
Network Learning ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Research on Nonlinear Identification Model of Wavelet Neural Network Trained by Artificial Fish Swarm Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1920-1923
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Wavelet Neural Network ◽  
Nonlinear Identification ◽  
Nonlinear Characteristics ◽  
Artificial Fish Swarm Algorithm ◽  
Model Based ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Identification Model

To describe the complex nonlinear characteristics of a system accurately, a Wavelet Neural Network (WNN) identification model based on Artificial Fish Swarm (AFS) algorithm is proposed. In the identification model, AFS algorithm is introduced to optimize the parameters combination of the network for the satisfactory WNN model. The simulation shows that, the proposed method is a good nonlinear identification capability, and is feasible to identify the nonlinear system.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

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