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Neural Networks Modeling and Control
Applications for Unknown Nonlinear Delayed Systems in Discrete Time
An in-depth review of modeling and control of discrete-time unknown nonlinear delayed systems based on artificial neural networks
Jorge D. Rios (Author), Alma Y Alanis (Author), Nancy Arana-Daniel (Author), Carlos Lopez-Franco (Author), Edgar N. Sanchez (Series edited by)
9780128170786
Paperback, published 20 January 2020
158 pages, Approx. 240 illustrations
23.4 x 19 x 1.2 cm, 0.34 kg
Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.
1. Introduction2. Mathematical preliminaries3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays7. Concluding remarks and future trends AppendixA. Artificial neural networksB. Linear induction motor prototypeC. Differential robot prototype
Subject Areas: Biomedical engineering [MQW]
