The Hybrid Intelligent Systems for Non-linear Dynamical Systems
DOI:
https://doi.org/10.59615/ijie.3.1.55DOR:
https://dorl.net/dor/20.1001.1.27831906.2023.3.1.6.2Keywords:
Hybrid Intelligent Systems, Non-linear Dynamical Systems, Modeling, Simulation, Control, Soft Computing, Neural Networks, Fuzzy Logic, Genetic Algorithms, Chaos TheoryAbstract
This research paper focuses on the use of advanced hybrid intelligent systems for modeling, simulation, and control of complex systems with non-linear behavior. Non-linear dynamical systems, which are prevalent in various industries, present unique challenges that require sophisticated solutions. Hybrid intelligent systems, combining multiple innovative techniques from the field of Soft Computing, have shown great promise in addressing these challenges. In this paper, we provide a comprehensive overview of hybrid intelligent systems and their advantages in dealing with non-linear dynamical systems. We explore the integration of different Soft Computing methodologies, such as Neural Networks, Fuzzy Logic, Genetic Algorithms, and Chaos Theory, to create powerful hybrid systems. We present real-world case studies and experimental results to showcase the effectiveness of these hybrid systems in modeling, simulation, and control tasks. Finally, we discuss future research directions and challenges in this exciting field, emphasizing the importance of continued exploration and development of hybrid intelligent systems for non-linear dynamical systems.
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Copyright (c) 2023 Aditya Singh
This work is licensed under a Creative Commons Attribution 4.0 International License.
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