BP neural network classification method for solving nonlinear problem through single hidden layer

Abstract

The invention provides a BP neural network classification method for solving a nonlinear problem through a single hidden layer and a classifier thereof. The method comprises the steps that a BP neural network model is established, wherein the BP neural network model adopts a single hidden layer feedforward network; the weight parameter of the input end of the BP neural network model is optimized; and data are inputted to the BP neural network model to perform combined calculation with the optimized weight parameter, and finally classification and identification of the data can be realized at the output end of the BP neural network model. The problems of high resource consumption and high calculation cost caused by solving the nonlinear separable problem through multiple hidden layers can be reduced so that the working efficiency of the neural network can be enhanced.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a BP neural network classification method using a single hidden layer to solve nonlinear problems. Background technique [0002] BP (Back Propagation) neural network was proposed by a team of scientists headed by Rumelhart and McCelland in 1986. It is a multi-layer feed-forward network trained by the error back propagation algorithm and is one of the most widely used neural network models. The BP neural network is divided into a single hidden layer feedforward network and a multi-hidden layer feedforward network. The single hidden layer feedforward network can also be called a three-layer feedforward network, namely: input layer, middle layer (also called hidden layer) and output layer. Traditional theory holds that a single-layer feedforward neural network can only solve linearly separable problems, and a network that can solve nonlinear problems must be a multi-layer n...

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Problems solved by technology

[0005] For this reason, the object of the present invention is to propose a kind of BP neural network classification method and the classifier that use single hidden layer to solve nonlinear problem, can reduce the resource consumption that uses multi-hidden layer to solve nonlinear separable problem to bring greatly, High computational cost and other issues, improve the work efficiency of neural network

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