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Title: A novel neural network classifier using beetle antennae search algorithm for pattern classification
Authors: Wu, Q
Ma, ZP
Xu, G
Li, S 
Chen, DC
Keywords: Beetle antennae search (BAS) algorithm
Pattern classification
Artificial neural networks (ANNs)
Neural network classifier (NNC)
Training algorithms
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 64686-64696 How to cite?
Journal: IEEE access 
Abstract: Traditional training algorithms in artificial neural networks (ANNs) show some inherent weaknesses, such as the possibility of falling into local optimum, slow learning speed, and the inability to determine the optimal neuronal structure. To remedy the deficiencies of traditional neural networks, this paper proposes a novel neural network classifier (NNC) using the beetle antennae search (BAS) algorithm, termed BASNNC. The BAS algorithm is explored to optimize the weights of the NNC. The network of the proposed BASNNC adopts three-layer structure, including an input layer, a hidden layer, and an output layer. Quite differing from the traditional training algorithm using a principle of gradient descent, the weights between the hidden and output layers are optimized by the BAS algorithm, which effectively improves the computational speed of the classifier. The numerical studies, applications to pattern classification and comparisons with an error back-propagation neural network model, show that the proposed BASNNC has faster computational speed and higher classification accuracy.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2917526
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Post with permission of the publisher.
The following publication Q. Wu, Z. Ma, G. Xu, S. Li and D. Chen, "A Novel Neural Network Classifier Using Beetle Antennae Search Algorithm for Pattern Classification," in IEEE Access, vol. 7, pp. 64686-64696, 2019 is available at
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