TITLE:
Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task
AUTHORS:
Hong Hu, Liang Pang, Dongping Tian, Zhongzhi Shi
KEYWORDS:
Artificial Brain Research; Brain-Like Computer; Fuzzy Logic; Neural Network; Machine Learning; Hopfield Neural Network; Bounded Fuzzy Operator
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.3 No.4,
October
8,
2013
ABSTRACT: 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.