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A comparative study of neural network structures in identification of nonlinear systems

Authors
Journal
Mechatronics
0957-4158
Publisher
Elsevier
Publication Date
Volume
9
Issue
3
Identifiers
DOI: 10.1016/s0957-4158(98)00047-6
Disciplines
  • Computer Science

Abstract

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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