期刊名称：International Journal of Intelligence Science
印刷版ISSN：2163-0283
电子版ISSN：2163-0356
出版年度：2013
卷号：3
期号：4
页码：145-161
DOI：10.4236/ijis.2013.34016
出版社：Scientific Research Publishing
摘要：Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.
