一种多权值神经元覆盖网络的构造方法

Constructive Algorithm of Multi-Weight Neuron Covering Networks

仿生模式识别利用多权值神经元覆盖网络构造模式类的覆盖来进行相应事物的识别.但在构造多权值神经元覆盖网络的过程中,关于构造神经元个数的确定方法没有相关讨论,即需要使用多少个神经元才能完成对模式类的覆盖.较多的神经元在精确的对模式类进行覆盖同时,也增大了网络的复杂度.文中提出了一种多权值神经元覆盖网络的构造方法.在保持神经网络对模式类的覆盖能力的基础上采用尽量少的神经元,从而能有效的降低神经网络构造代价.最后,通过实验作者验证了算法的有效性.

Biomimetic Pattern Recognition theory uses multi-weight neuron covering networks to perform recognition tasks.However,there are few discussions on the constructive methods of multi-weight neuron covering networks,namely how many neurons are needed to fulfill the covering of a data set.A large number of neurons can cover the data set precisely,but it will make the network be more complicated.In this paper,we propose a constructive algorithm for multi-weight neuron covering networks.The algorithm can construct a covering network with least neurons and maintain the covering ability of the networks simultaneously.The experiments show the efficiency of the proposed algorithm.