| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
M. Han and Jianhui Xi
ABSTRACT
This paper studies how to train a new feed-forward neural network, radial basis perceptron (RBP) neural network, for distinguishing different sets in RL. RBP neural network is based on radial basis function (RBF) neural network and perceptron neural network. It has two hidden layers where the nodes are not fully connected but use selective connection. A training algorithm corresponding to the structure of RBP network is presented. It adopts the input–output clustering (IOC) method to provide an efficient and powerful procedure for constructing a RBP network that generalizes very well. First, during the learning procedure, RBP neural network adopts IOC method to define the number of units of hidden layers and select centers. Second, the width parameter of centers is self-adjustable according to the information included in the learning samples. The effectiveness of this network is illustrated using an example taken from applications for component analysis of civil building materials. Simulation shows that RBP neural network can be used to predict the components of civil building materials successfully and gets good generalization ability |
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