scholarly journals Nonlinear Modeling Method Based on RBF Neural Network Trained by AFSA with Adaptive Adjustment

2017 ◽  
Author(s):  
Xu-Sheng Gan ◽  
Zhi-bin Chen ◽  
Ming-gong Wu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Nonlinear Modeling ◽  
Modeling Method ◽  
Adaptive Adjustment

Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

2015 ◽  
Vol 713-715 ◽  
pp. 1855-1858 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Nonlinear Function ◽  
Training Algorithm ◽  
Step Size ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Adaptive Adjustment ◽  
Swarm Algorithm

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

An Online Modeling Method for Real-Time Thermal Error Compensation on High-Speed Machines Based on RBF Neural Network Theory

2010 ◽  
Vol 455 ◽  
pp. 606-611 ◽  
Author(s):  
Hong Tao Zhang ◽  
Hui Jiang ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Rbf Neural Network ◽  
Thermal Characteristics ◽  
Thermal Error ◽  
Modeling Method ◽  
Learning Ability ◽  
Online Modeling ◽  
Set Up ◽  
Neural Network Theory

This paper studies the modeling method based on RBF (Radial-Basis Function) neural network according to its learning ability, and a new neural network online model has been set up. The comparison and analysis result of the case studies shows that, when changing the working condition, the compensation effect of online modeling method is better than offline modeling method and the online model can better reflect the thermal characteristics of High-speed machine tool.

Hopfield Neural Network Modeling Method Based on Subset KPCA with FCMC Clustering

2014 ◽  
Vol 1049-1050 ◽  
pp. 1658-1661
Author(s):  
Xiang Jie Luo ◽  
Xiao Yong Li ◽  
Hong Qu ◽  
Yue Bo Meng
Keyword(s):  
Neural Network ◽  
Nonlinear Modeling ◽  
Network Modeling ◽  
Hopfield Neural Network ◽  
Modeling Method ◽  
Neural Network Modeling ◽  
Fuzzy C Means Clustering ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
Better Than

In order to improve the performance of nonlinear modeling, a Hopfield neural network modeling method based on Subset Kernel Principal Components Analysis (SubKPCA) with Fuzzy C-Means Clustering (FCMC) is proposed. The simulation result shows that, the performance of the proposed method is better than that of Hopfield neural network based on KPCA. It also is effective and feasible to establish the model for the estimation of missing flight data.

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