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    一种可自行学习的联想记忆神经网络

    A Associate Memorizing Neural Network With Self-Learning

    • 摘要: 本文在生物神经突触特性的基础上,提出非线性神经突触神经元的概念,并以此为根据,构造了一种用原电池等元件组成的可自行学习联想记忆神经网络模型。这种模型可按Hebb规则学习,学习机制由网络本身完成,由于网络权重非线性的引入和使用原电池作为记忆元件,使此网络能以较简单的结构实现网络的自身学习功能。文中对网络的记忆容量和此种网络在以特定的学习方式学习后与Hopfield 网络的等效性进行了讨论。以电池连接的模型实验(四个神经元)说明此种网络结构是可行的。

       

      Abstract: In this paper,a self-learning,associatively memorizing network is proposed.Basingon biological concepts,the synapse in the net is simulated,each with 4 diodes and 4 batteriesThe learning process of the net is carried out by charging a specific battery in each synapse andthe process is controlled by adjusting the duration of charging and input sample pattern.Thestructure of the synapse is simple.The network composed of such synapses is capable of self-learning and is competent to develop into large scale network It is also capable of simultaneous(parallel) multi-input learning instead of scanning as most computers do Its memory capacity isdiscussed and the function of a preliminary 4-neuron,16-synapse model network has shown inte-resting features

       

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