Extreme learning machine classification algorithm based on improved crow search algorithm

Abstract

The invention relates to the technical field of extreme learning machine classification algorithms, in particular to an extreme learning machine classification algorithm based on an improved crow search algorithm. The method includes the following steps that an ELM network model is built, an ICSA algorithm is adopted, input weights and threshold values generated randomly by the ELM model are optimized, global and local search performance is balanced by introducing an AP value dynamic gradient function, a Levy flight search method is introduced to avoid blind search, a multi-individual variable factor weighted learning method is introduced to guarantee population diversity, and the global and local search performance is improved. And an adjacent-generation dimension crossing method is introduced to enhance the quality of the optimal individual food storage position, so that a local optimal value is prevented from being obtained, and an accurate prediction result is realized. According to the method, a series of defects caused by randomly generating input weights and threshold values are overcome, the ELM model classification precision is improved, and when ELM model parameters are optimized, based on a traditional CSA algorithm, global and local search performance is balanced by introducing an AP value dynamic gradient function, and blind search is avoided by introducing a Levy flight search method.

Description

technical field [0001] The invention relates to the technical field of extreme learning machine classification algorithms, in particular to an extreme learning machine classification algorithm based on an improved crow search algorithm. Background technique [0002] The extreme learning machine (ELM) has the same network structure as the single hidden layer feedforward neural network, and has fixed advantages in dealing with multi-classification problems. Unlike Support Vector Machine (SVM), which requires multiple classifiers to vote for multi-classification problems, ELM only needs to use one network to realize multi-classification. ELM has fast operation speed and low computational complexity, because it does not solve complex quadratic optimization like SVM, and unlike BP neural network through iterative solution, it only needs to set the number of hidden layer nodes to obtain good performance. Network frameworks can easily handle multi-classification problems. [0003...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the generalization performance of the algorithm is low due to the random generation of ELM input weights and thresholds, a classification algorithm for extreme learning machines based on the improved crow search algorithm (ICSA) is proposed

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