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Start Over Creator Ling, Xiaofeng

1. A two-stage learning method to configure RBF centers and widths in dynamic environment employing immune operations

Creator:
Ling, Xiaofeng, Gong, Xinbao, Zang , Xiaogang, and Ronghong, Jin
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
RBF neural network, dynamics problems, and immune evolutionary mechanism
Language:
English
Description:
This paper proposes an immunity-based RBF training algorithm for nonlinear dynamic problems. Exploiting the locally-tuned structure of RBF network through immunological metaphor, a two-stage learning technique is proposed to configure RBF centers and widths in the hidden layer. Inspired by affinity maturation process of immune response, immune evolutionary mechanism (IEM) with memory operations is implemented in the learning stages to dynamically fine-tune the network performance. Experiment results also demonstrate that the algorithm has reached good performance with relatively low computational efforts in dynamic environments.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

2. An evolutionary RBF network configuration using adaptive width adjustment based on vaccination

Creator:
Zang, Xiaogang, Gong, Xinbao, Ling, Xiaofeng, Chang, Cheng , and Tang , Bin
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Computational learning theory, evolutionary computation, neural networks, RBF networks, and vaccination operation
Language:
English
Description:
In this paper, a mechanism of adaptive width adjustment based on immunological vaccination is proposed for the evolutionary training of RBF neural networks. Inspired by the vaccination process of the natural immune system, the algorithm implements an individual-orientated adaptation of the width in training stages to optimize the potential solutions, therefore reinforces the evolutionary capability and efficiency. A two-layer genotype-coding scheme, which enables a simultaneous evolution of network structure and parameters, is presented to achieve a compact and consistent-in-form solution. The proposed learning strategy is tested on several benchmark problems and results demonstrate promise.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public