caa a22 4500 605469512 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1292-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1292-y Training neural networks via simplified hybrid algorithm mixing Nelder-Mead and particle swarm optimization methods [Elektronische Daten] [Shih-Hui Liao, Jer-Guang Hsieh, Jyh-Yeong Chang, Chin-Teng Lin] In this paper, a new and simplified hybrid algorithm mixing the simplex method of Nelder and Mead (NM) and particle swarm optimization algorithm (PSO), abbreviated as SNM-PSO, is proposed for the training of the parameters of the Artificial Neural Network (ANN). Our method differs from other hybrid PSO methods in that, $$n+1$$ n + 1 particles, where $$n$$ n is the dimension of the search space, are randomly selected (without sorting), at each iteration of the proposed algorithm for use as the initial vertices of the NM algorithm, and each such particle is replaced by the corresponding final vertex after executing the NM algorithm. All the particles are then updated using the standard PSO algorithm. Our proposed method is simpler than other similar hybrid PSO methods and places more emphasis on the exploration of the search space. Some simulation problems will be provided to compare the performances of the proposed method with PSO and other similar hybrid PSO methods in training an ANN. These simulations show that the proposed method outperforms the other compared methods. Springer-Verlag Berlin Heidelberg, 2014 Artificial Neural Network (ANN) nationallicence Particle swarm optimization (PSO) nationallicence Simplex method of Nelder and Mead (NM) nationallicence Liao Shih-Hui Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut Hsieh Jer-Guang Department of Electrical Engineering, I-Shou University, Kaohsiung County, Taiwan aut Chang Jyh-Yeong Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut Lin Chin-Teng Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 679-689 1432-7643 19:3<679 2015 19 500 https://doi.org/10.1007/s00500-014-1292-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1292-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liao Shih-Hui Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut NATIONALLICENCE 700 1- Hsieh Jer-Guang Department of Electrical Engineering, I-Shou University, Kaohsiung County, Taiwan aut NATIONALLICENCE 700 1- Chang Jyh-Yeong Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut NATIONALLICENCE 700 1- Lin Chin-Teng Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, ROC aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 679-689 1432-7643 19:3<679 2015 19 500