Extreme learning machine classifying method based on waveform addition cuckoo optimization

Abstract

The invention relates to an extreme learning machine classifying method based on waveform addition Cuckoo optimization. The extreme learning machine classifying method mainly comprises the steps that (I) a training sample matrix is established; (II) M initial parasitic nests are generated on each hidden node; (II) the classifying accuracy of a waveform addition extreme learning machine classifying model is solved; (IV) training samples are randomly and equally divided into parts (please see the number of the parts in the specification), and the classifying accuracy output value of the extreme learning machine classifying model verified in a cross mode is solved; (V) an inverse hyperbolic sine function and a Morlet wavelet function are superposed to serve as an excitation function of the extreme learning machine, the waveform addition extreme learning machine classifying model is structured, and the current generation classifying accuracy of a Cuckoo algorithm is obtained; (VI) a next generation result of the Cuckoo algorithm is solved, and parasitic nests are newly established with the probability Pa; (VII) repeated iteration is conducted, whether the iteration is ended is judged, an optimal extreme learning machine classifying model is established if ending conditions are met, and the optical extreme learning machine classifying model is used for classifying unknown samples. The extreme learning machine classifying method is low in calculation complexity, high in efficiency, stable in classifying performance, high in accuracy and high in global optimization and generalization performance.

Description

technical field [0001] The invention belongs to the technical field of computer intelligence and neural network. The invention relates to an extreme learning machine classification method, in particular to an extreme learning machine classification method based on waveform superposition cuckoo optimization. Background technique [0002] Most of the traditional feedforward neural network learning methods (such as BP neural network algorithm) use the gradient descent method to optimize. The extreme learning machine (Extreme Learning Machine, ELM) is different. It was proposed by Huang et al. in 2006 based on the Moore-Penrose (MP) generalized inverse matrix theory. It is a simple and effective single hidden layer feedforward neural network learning. Method (Single-Hidden-Layer Feedforward Neural Networks, SLFNs). This method has the same global approximation properties as the neural network. By randomly generating network input weights and hidden layer neurons, and setting t...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a kind of extreme learning machine classification method based on waveform superposition cuckoo optimization for the classification effect of the existing extreme learning machine classification method is unstable and the classification accuracy is low. This classification method uses two kinds of waveforms Superposition is used as the excitation function of waveform superposition extreme learning machine instead of the single excitation function of standard extreme learning machine, which increases the fast convergence performance and the dynamic approximation ability of high and low frequency signals. At the same time, it combines the cuckoo optimization algorithm to optimize the parameters of extreme learning machine and establish the best waveform. Stacked Extreme Learning Machine Classification Models

Compared with the traditional extreme learning machine classification modeling method, the waveform superposition extreme learning machine classification method of the present invention has the characteristics of higher classification accuracy, faster parameter adjustment, and strong optimization ability within an acceptable time. The classification results of standard extreme learning machines are unstable, and the reliability of classification is poor.

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