Gradient descent and generalized inverse-based complex-valued neural network training method

Abstract

The invention relates to a gradient descent and generalized inverse-based complex-valued neural network training method. The method includes the following steps that: step 1, a single-hidden layer complex-valued neural network model is selected; step 2, the gradient descent and generalized inverse are utilized to calculate a weight matrix and a weight vector in the single-hidden layer complex-valued neural network model; step 3, the network parameters of the complex-valued neural network model are obtained according to the weight matrix and the weight vector, and mean square error is calculated, and 1 is added to the number of iterations, and the method returns to the step 2. According to the method of the invention, the input weight of a hidden layer is generated through the gradient descent, and the output weight of the hidden layer is always solved by the generalized inverse. The method of the invention has the advantages of small number of iterations, short corresponding training time, high convergence speed and high learning efficiency, and just needs few hidden layer nodes. Therefore, the method of the invention can reflect the performance of the complex-valued neural network more accurately compared with a BSCBP (Batch Split-Complex Backpropagation Algorithm) method and a CELM (Complex Extreme Learning Machine) method.

Description

technical field [0001] The invention belongs to the technical fields of image processing, pattern recognition and communication transmission, and in particular relates to a complex-valued neural network training method based on gradient descent method and generalized inverse. Background technique [0002] In image processing, pattern recognition and communication transmission, the method of using neural network modeling for sample training and testing has a wide range of applications. In the neural network model modeling in the training samples, the neural network signals (input signal, output signal and weight parameter) can be real-valued or complex-valued, so the neural network is divided into real-valued neural network and complex-valued neural network. Most of the existing neural network modeling methods are real-valued neural network models, but with the rapid development of electronic information science, complex-valued signals appear more and more frequently in engin...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the above problems, the present invention overcomes the problems that the traditional complex-valued neural network training method cannot simultaneously solve the problems of slow training speed, low precision and too many nodes in the network hidden layer, and provides a complex-valued neural network based on gradient descent method and generalized inverse Neural network training method (Gradient based Generalized Complex Neural Networks, referred to as GGCNN)

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