Second-order hybrid construction method and system of complex-valued forward neural network

Abstract

The invention relates to a second-order hybrid construction method and system for a complex-valued forward neural network. The second-order hybrid construction method comprises the following steps: initializing the structure and parameters of the complex-valued neural network according to a given task; adjusting parameters in the complex-valued neural network by using a complex-valued second-orderhybrid optimization algorithm, and judging whether a construction termination condition is met or not; verifying the generalization performance of the complex-valued neural network, storing the number of current hidden layer neurons and all parameter values of the complex-valued neural network, judging whether the adding standard of the hidden layer neurons is met or not, if yes, adding one hidden layer neuron to the current model by utilizing a complex-valued increment construction mechanism, and otherwise, adding one hidden layer neuron to the current model by utilizing a complex-valued increment construction mechanism; calculating a new hidden layer output matrix and an error function on the basis of current training, and returning to the previous step; if not, directly returning to the previous step; and further finely adjusting the parameters by using the complex-valued second-order hybrid optimization algorithm to obtain an optimal complex-valued neural network model. Accordingto the invention, the complex-valued neural network model with a reasonable structure can be constructed automatically.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence and pattern recognition, in particular to a second-order hybrid construction method and system of a complex-valued feedforward neural network. Background technique [0002] Artificial neural network has powerful self-learning, self-organization, self-adaptation and nonlinear function approximation capabilities, and can learn rules and knowledge from seemingly disordered massive data. In recent years, the research of real-valued neural network has achieved very fruitful results. However, in some engineering fields, it is often necessary to analyze and process complex signals. With its powerful computing power and good generalization performance, complex-valued neural network has received more and more attention, and has been widely used in various industrial fields, such as radar signal processing, medical image processing, channel State prediction, pattern recognition, etc. [0...

AI Technical Summary

Problems solved by technology

However, the first-order complex-valued optimization algorithm has disadvantages such as slow convergence speed and easy to fall into local minimum.

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