Two Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate
Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JACET-1-1_005
تاریخ نمایه سازی: 18 تیر 1398
Abstract:
Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon or slower convergence speed due to larger fixed or smaller fixed learning rate respectively. The present research deals with offering two solutions for this problem. The original idea of the present research is using changeable learning rate at each state of training phase in the CMAC model. The first algorithm deals with a new learning rate based on reviation of learning rate. The second algorithm deals with number of training iteration and performance learning, with respect to this fact that error is compatible with inverse training time. Simulation results show that this algorithms have faster convergence and better performance in comparison to conventional CMAC model in all training cycles.
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Authors
Nazal Modhej
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Khouzestan.
Mohammad Teshnehlab
Khaje Nasir Toosi University of Technology
Mashallah Abbasi Dezfouli
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Khouzestan