AN OPTIMIZED EXTREME LEARNING MACHINE USING ARTIFICIAL CHEMICAL REACTION OPTIMIZATION ALGORITHM

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Keywords: Extreme learning machine (ELM), artificial chemical reaction optimization algorithm (ACROA), single-hidden-layer feed-forward neural network (SLFN); learning algorithm; classification.

Abstract

Extreme Learning Machine (ELM) is a simple learning algorithm for singlehidden-layer feed-forward neural network. The learning speed of ELM can be
thousands of times faster than back-propagation algorithm, while obtaining
better generalization performance. However, ELM may need high number of
hidden neurons and lead to ill-condition problem due to the random
determination of the input weights and hidden biases. In order to surmount the
weakness of ELM, this paper proposes an optimization scheme for ELM based on
artificial chemical reaction optimization algorithm (ACROA). By using ACROA to
optimize the hidden biases and input weights according to both Root mean
squared error and the Norm of output weights, the classification performance of
ELM will be improved. The experimental result on several real benchmark
problems demonstrates that the proposed method can attain higher
classification accuracy than traditional ELM and other evolutionary ELMs.

điểm /   đánh giá
Published
2020-11-12
Section
RESEARCH AND DEVELOPMENT