Hardware circuit of recursive neural network of LS-SVM classification and returning study and implementing method

Abstract

The invention discloses an LS-SVM classification and regression study recursive neural network hardware circuit and a realization method; the method combines the LS-SVM method with the recursive neural network to is deduce a dynamic equation and a topological structure describing the neural network, and further establishes a hardware circuit for realizing the recursive neural network, so that the hardware circuit is used to realize the least square support vector machine algorithm. Compared with the existing network, the LS-SVM classification and regression study recursive neural network of the invention eliminates the non-linear part of the network, so the neural network structure is simplified and the SVM training speed is greatly improved; meanwhile, the LS-SVM study neural network provided by the invention can realize classification and regression, on the basis of nearly unchanging the topological structure.

Description

LS-SVM Classification and Regression Learning Recursive Neural Network Hardware Circuit and Implementation Method technical field The invention belongs to the technical field of pattern recognition, and relates to a LS-SVM classification and regression learning recursive neural network hardware circuit, and also relates to a realization method of the hardware circuit. Background technique Support vector machines (Support Vector Machines, SVMs) adopt the idea and method of structural risk minimization, and have been widely used as A tool for classification and regression. At present, the research on SVM mainly focuses on theoretical research and algorithm optimization. In contrast, there are relatively few researches on its application research and algorithm implementation, and there are only limited experimental research reports at present. At the same time, most of these algorithms can only be realized by computer software, not suitable for the realization of analog har...

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Problems solved by technology

Another object of the present invention is to provide a method for implementing LS-SVM classification and regression learning recursive neural network hardware circuits, so that the problems of classification and regression learning can be solved by simulating hardware circuits

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