eprintid: 10956 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/09/56 datestamp: 2023-11-09 16:37:34 lastmod: 2023-11-09 16:37:34 status_changed: 2023-11-09 16:32:36 type: article metadata_visibility: show creators_name: Melo, H. creators_name: Zhang, H. creators_name: Vasant, P. creators_name: Watada, J. title: Training method for a feed forward neural network based on meta-heuristics ispublished: pub keywords: Backpropagation; Gaussian distribution; Heuristic methods; Multimedia signal processing; Neural networks; Optimization; Signal processing, Back propagation neural networks; Cauchy distribution; Meta heuristics; Network weights; Optimized parameter; Particle swarm optimization algorithm; Training algorithms; Training methods, Particle swarm optimization (PSO) note: cited By 0; Conference of 13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2017 ; Conference Date: 12 August 2017 Through 15 August 2017; Conference Code:195379 abstract: This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima. © Springer International Publishing AG 2018. date: 2018 publisher: Springer Science and Business Media Deutschland GmbH official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026662697&doi=10.1007%2f978-3-319-63859-1_46&partnerID=40&md5=4ced0da20f5776abff3e471bacd835ca id_number: 10.1007/978-3-319-63859-1₄₆ full_text_status: none publication: Smart Innovation, Systems and Technologies volume: 82 pagerange: 378-385 refereed: TRUE isbn: 9783319638584 issn: 21903018 citation: Melo, H. and Zhang, H. and Vasant, P. and Watada, J. (2018) Training method for a feed forward neural network based on meta-heuristics. Smart Innovation, Systems and Technologies, 82. pp. 378-385. ISSN 21903018