TY - JOUR ID - 73033 TI - Wavelet Neural Network with Random Wavelet Function Parameters JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Bazoobandi, H. AD - Department of Computer Engineering, Esfarayen University of Technology, Esfarayen, North Khorasan, Iran Y1 - 2017 PY - 2017 VL - 30 IS - 10 SP - 1510 EP - 1516 KW - Wavelet Neural Network KW - Training algorithm KW - Moore KW - Penrose inverse KW - random parameter values DO - N2 - The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden layer and output layer of the network; meanwhile, the wavelet function parameters are randomly assigned and kept fixed during the training process. Besides the simplicity and speed of the proposed one-step algorithm, the experimental results verify the performance of the proposed method in terms of final model accuracy and computational time. UR - https://www.ije.ir/article_73033.html L1 - https://www.ije.ir/article_73033_7aed217ded6382805b29967c9dc805ff.pdf ER -