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Multi hidden layer extreme learning machine optimised with batch intrinsic plasticity Accession number: 20191706826940 Authors: Pang, Shan 1 ; Yang, Xinyi 2 Author affiliations : 1 College of Information and Electrical Engineering, Ludong University, Yantai; 264025, China 2 Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai; 264001, China Corresponding author: Pang, Shan (pangshanpp@163.com) Source title: International Journal of Computational Science and Engineering Abbreviated source title: Int. J. Comput. Sci. Eng. Volume: 18 Issue: 4 Issue date: 2019 Publication Year: 2019 Pages: 375-382 Language: English ISSN: 17427185 E-ISSN: 17427193 Document type: Journal article (JA) Publisher: Inderscience Enterprises Ltd. Abstract: Extreme learning machine (ELM) is a novel learning algorithm where the training is restricted to the output weights to achieve a fast learning speed. However, ELM tends to require more neurons in the hidden layer and sometimes leads to ill-condition problem due to random selection of input weights and hidden biases. To address these problems, we propose a multi hidden layer extreme learning machine optimised with batch intrinsic plasticity (BIP) scheme. The proposed algorithm has a deep structure and thus learns features more efficiently. The combination of BIP scheme helps to achieve better generalisation ability. Comparisons with some state-of-the-art ELM algorithms on both regression and classification problems have verified the performance and effectiveness of our proposed algorithm. Copyright © 2019 Inderscience Enterprises Ltd. Number of references: 24 Main heading: Learning algorithms Controlled terms: Knowledge acquisition - Learning systems - Network layers - Neural networks Uncontrolled terms: Deep structure - Extreme learning machine - Generalisation - Hidden layers - Ill-conditions - Intrinsic plasticity - Random selection - State of the art Classification code: 723Computer Software, Data Handling and Applications - 723.4Artificial Intelligence DOI: 10.1504/IJCSE.2019.099075 |
2Â¥2019-06-27 10:07:25
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