Self-generated neural network construction method

Abstract

A self-generated neural network construction method comprises the following steps of 1 adding a stimulation signal; 2 evaluating the output intensity of a nerve cell, determining the connection direction of the nerve cell, forming the network connection continuously, and finally generating an initial network; 3 calculating the position and the probability connected with a target nerve cell; 4 determining whether the current network generation process stops, if yes, turning to a step 5, otherwise turning to the step 2; 5 optimizing the network connection via an optimization algorithm; 6 determining whether simulation still needs to be added, if not ending, otherwise, turning to the step 1. The network provided by the present invention is a self-generated network, and the influence of the artificial subjective factors can be reduced effectively. In addition, the generation process of the network takes the biological neural theory as basis, so that the neural brain can be searched possibly and further to realize the real intelligence.

Description

technical field [0001] The invention belongs to the technical field of neural network calculation, relates to a software-oriented modeling method and a hardware-realized neural network self-generation method, in particular to a self-generated neural network construction method. Background technique [0002] With the development of computers today, both computing power and power consumption have been substantially improved in performance. But with the diversification of people's needs, the current computer structure exposes more and more problems. There are two types of information processing problems provided by nature: structured and unstructured. A structural problem means that it can be clearly and strictly described in mathematical language, and the realization algorithm of the problem can be formulated and mapped to a computer program, and then processed by computer instructions. Traditional von Neumann machines far outperform humans in solving such problems. Non-str...

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Problems solved by technology

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the object of the present invention is to provide a method for constructing a self-generated neural network, which maps the characteristics of the biological neural model and the appropriate heuristic algorithm to the silicon-based circuit, so that the neural network can pass the model Self-generating and self-organizing, avoiding the shortcomings of artificial neural networks

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