A comparative study of neural network structures in identification of nonlinear systems |
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| Affiliation: | 1. University of Chicago Medical Center, Chicago, Illinois;2. Department of Medicine, University of Chicago Medical Center, Chicago, Illinois;3. Baylor College of Medicine, Center for Medical Ethics and Health Policy, Houston, TX;4. Department of Surgery, University of Chicago Medical Center, Chicago, Illinois;1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. Applied Math Department, Faculty of Mathematics, University of Waterloo, Waterloo, Canada;3. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran |
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| Abstract: | This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge–Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a three degrees of freedom anthropomorphic robotic manipulator. |
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