A neural network generation method and device

Abstract

The invention provides a neural network generation method and device, and the method comprises the steps: obtaining neural network generation parameters which comprise a neuron clustering number, a neuron density parameter, a distribution space size parameter, and a neuron total number; Obtaining a basic neuron according to the neuron clustering number; Generating a neural network by taking the basic neurons as a clustering center according to a preset generation rule, the number of the neurons in the neural network being the same as the total number of the neurons, each neuron in the neural network being bidirectionally interconnected with its adjacent neuron, and each neuron in the neural network being connected with itself at a preset probability; And setting an input node and an outputnode which are connected with the neural network. The neural network generated by the method can be self-clustered, the internal dynamic characteristics can be enhanced, the coupling degree between neurons is relatively low, and the connection structure of the neural network is more similar to that of a biological network compared with the neural network obtained in the prior art.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a neural network generation method and device. Background technique [0002] The construction of the neural network is based on the premise of the application of the neural network. In the past ten years, the biological neural network system that simulates the biological neural network has excellent performance in the fields of identification, decision and prediction. The biological neural network has better intelligence and adaptability by simulating the biological neural network, but usually the completely random connection of each neuron in the neural network leads to a high degree of coupling inside the neural network and insufficient dynamic characteristics, resulting in The adaptiveness of the neural network is difficult to improve and the output error is difficult to reduce. Contents of the invention [0003] In order to solve the above technical problems, the present inv...

AI Technical Summary

Problems solved by technology

The biological neural network has better intelligence and adaptability by simulating the biological neural network, but usually the completely random connection of each neuron in the neural network leads to a high degree of coupling inside the neural network and insufficient dynamic characteristics, resulting in The adaptiveness of the neural network is difficult to improve and the output error is difficult to reduce

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