Modeling and control of non-linear systems using soft computing techniques |
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| Affiliation: | 1. Faculty of Electrical Engineering, USTO, BP 1505 El-Mnaouar, Oran 31000, Algeria;2. Institut für Elektrische Energiesysteme, Universität Magdeburg, Postfach 4120, 39016 Magdeburg, Germany;1. Research Scholar, SGGS Institute of Engineering and Technology, Vishnupuri, Nanded 431606, India;2. Department of Instrumentation Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded 431 606, India;3. Reactor Control Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085, India;1. Robotics and Automation Department, CSIR-Central Mechanical Engineering Research Institute, Durgapur, PIN – 713209, India;2. Electrical Engineering Department, Jadavpur University, Kolkata, PIN – 700032, India;1. Piri Reis University, Faculty of Science and Letters, 34940 Tuzla, Istanbul, Turkey;2. CNISM and Dipartimento di Fisica dellʼUniversità, I-98166 Messina, Italy;3. Dipartimento di Fisica dellʼUniversità and CNR-IOM, I-34151 Trieste, Italy;4. Scuola Normale Superiore, I-56126 Pisa, Italy;1. The Erosion/Corrosion Research Center, The University of Tulsa, USA;2. Chevron Energy Technology Company, USA;1. Mathematics Department, Mimar Sinan Fine Arts University, Istanbul, Turkey;2. School of Mathematics, University of Manchester, UK |
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| Abstract: | This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies. |
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