Double synaptic weight neuron theory and its application

Authors:
Wang Shou-jue;Chen Xu;Qin Hong;Li Weijun;Bian Yi
Affiliations:
Laboratory of Artificial Neural Networks, Institute of Semiconductors, Chinese Academy of Sciences;Laboratory of Artificial Neural Networks, Institute of Semiconductors, Chinese Academy of Sciences;Laboratory of Artificial Neural Networks, Institute of Semiconductors, Chinese Academy of Sciences;Laboratory of Artificial Neural Networks, Institute of Semiconductors, Chinese Academy of Sciences;Laboratory of Artificial Neural Networks, Institute of Semiconductors, Chinese Academy of Sciences
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 1
Cited 1

Radial basis functions for multivariable interpolation: a review

Algorithms for approximation

A Two-Pass Classification Method Based on Hyper-Ellipsoid Neural Networks and SVM's with Applications to Face Recognition

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)1 is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.