RBF neural network based on q-Gaussian function in function approximation

2011 ◽  
Vol 5 (4) ◽  
pp. 381-386 ◽  
Author(s):  
Wei Zhao ◽  
Ye San
Keyword(s):  
Neural Network ◽  
Function Approximation ◽  
Rbf Neural Network ◽  
Gaussian Function

Design of RBF Neural Networks Based on Adjustable Radius

2010 ◽  
Vol 439-440 ◽  
pp. 605-610
Author(s):  
Xiao Yong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Gaussian Function ◽  
Mean Square ◽  
Rbf Neural Networks ◽  
Data Points ◽  
Better Than

In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better than oRBFNN.

A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation

2005 ◽  
Vol 16 (1) ◽  
pp. 57-67 ◽  
Author(s):  
G.-B. Huang ◽  
P. Saratchandran ◽  
N. Sundararajan
Keyword(s):  
Neural Network ◽  
Function Approximation ◽  
Rbf Neural Network

Realization of RBF Neural Network with Local and Semi-Local Transfer Function Approximation and Classification

2014 ◽  
Vol 687-691 ◽  
pp. 1628-1632
Author(s):  
Mao Luo ◽  
Shao Yun Song
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Function Approximation ◽  
Rbf Neural Network ◽  
Dimensional Space ◽  
Training Data ◽  
Classification Analysis ◽  
Benchmark Approach ◽  
And Training

Incremental Neural Network (IncNet) structure is controlled by the growth and pruning, and the complexity of the match and training data. Dual radial transfer function is more flexible than other commonly transfer function used in artificial neural network. Recent improvements in the multi-dimensional space (having the N-1 parameters) to increase the rotation of the transfer function of the constant value. Based on the results of the benchmark approach and psychological classification analysis clearly shows than any other classification network model has a stronger generalization.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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