An electromyographic signal classification method based on a two-parameter kernel optimization type extreme learning machine

Abstract

The invention discloses a myoelectricity recognition method based on a two-parameter kernel optimization type extreme learning machine. The method comprises the following steps: firstly, extracting four paths of electromyographic signals and extracting corresponding average amplitude, variance, Wilson amplitude and wavelet energy coefficients, then fusing the characteristics, and finally, transmitting the fused characteristics to a dual-parameter optimization type extreme learning machine. According to the dual-parameter optimization type extreme learning machine, on the basis of the extreme learning machine, a Gaussian kernel function is introduced, all parameters are set and optimized by minimizing an output weight matrix, a neural network structure is constructed, and the problem of minimizing an output error of the extreme learning machine is converted into the problem of minimizing an output weight. Compared with a traditional extreme learning machine, the method has the advantages that the function approximation capability is stronger, the nonlinear classification processing capability is stronger, and compared with other common classifier algorithms, the method also has higher accuracy and shorter operation time.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a myoelectric recognition method based on a dual-parameter kernel optimized extreme learning machine. Background technique [0002] Pattern recognition methods are generally divided into six categories: statistical recognition methods, syntactic structure recognition methods, fuzzy recognition methods, artificial neural network recognition methods, template matching recognition methods, and support vector machine recognition methods [41]. Among them, the recognition methods of statistics, fuzzy, neural network and support vector machine are used more. [0003] While accurate and fast for a single linear discriminant analysis (LDA), for multiple-input and multiple-output systems, using linear discriminants becomes complicated. In order to solve this problem, many scholars have introduced the technique of "kernel function" to study the combination of kernel function and linear rec...

AI Technical Summary

Problems solved by technology

[0003] While accurate and fast for a single linear discriminant analysis (LDA), for multiple-input and multiple-output systems, using linear discriminant becomes complicated

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