摘要:
神经网络已广泛应用于模式识别、优化计算等领域.但是,人们为了寻求某一问题的神
经网络结构,往往采用穷举法,从而使得选择合适的神经网络结构随着问题规模的变大而变
得越来越困难.本文根据神经元状态的变化导致人脑的空间结构和状态变化的研究,在神经
网络中引入神经元的兴奋、抑制和突触修改机制、退化机制、死亡机制、自修复机制等,
通过神经网络的学习,自动生成解决某一具体问题的合适的神经网络结构.实验结果表明,
该方法是可行的、有效的,为神经网络结构的设计提供了一种新方法.
Abstract:
Neural networks are widely used in pattern recognition, optimization computation,
etc. But, for a given problem, in order to. get a neural'network structure,
designers usually use the method of trial and error. Thus the selection of neural
network structure is more and more difficult with the problem more complex. In
this paper, according to the research of neuron state change resulting in brain
space structure and brain state change, the excitation, inhibitation, synaptic
change mechanism, degradation mechanism, death mechanism and auto-repairing
mechanism of neuron are introduced in neural network. The suitable neural network
structure for a given problem can be built through neural network learning.
The experiment results have shown that the method is available and provides a
new way of designing neural network structure.
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