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.
Highlights
Driven by rapid ongoing advances in computer hardware, neuroscience and computer science, artificial brain research and development are blossoming [1]
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
The main contributions of this paper include: 1) we develop a novel hybrid designing approach of neural networks based on fuzzy Logic and Proximal Support Vector Machine Classifiers (PSVM) learning in the artificial brain designing, which greatly simplifies the designing of large scale artificial brain; 2) a novel concept about fuzzy logical framework of neural network is firstly proposed; 3) instead of the linear mapping in [8], a novel nonlinear neural fuzzy logical texture feature extracting, which can effectively increase Texture information entropy (TIE), is introduced in the task of haze free application
Summary
Driven by rapid ongoing advances in computer hardware, neuroscience and computer science, artificial brain research and development are blossoming [1]. The visual functions simulated by “Blue Brain Project” and “Spaun” are so simple that they are nothing in the traditional pattern recognition This neural network and design it . The main contributions of this paper include: 1) we develop a novel hybrid designing approach of neural networks based on fuzzy Logic and Proximal Support Vector Machine Classifiers (PSVM) learning in the artificial brain designing, which greatly simplifies the designing of large scale artificial brain; 2) a novel concept about fuzzy logical framework of neural network is firstly proposed; 3) instead of the linear mapping in [8], a novel nonlinear neural fuzzy logical texture feature extracting, which can effectively increase TIE, is introduced in the task of haze free application.
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