Modelling, Simulation and Control of Non-linear Dynamical Systems
An Intelligent Approach Using Soft Computing and Fractal Theory
Series: Numerical Insights;
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88 567 Ft
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 25 October 2001
- ISBN 9780415272360
- Binding Hardback
- No. of pages262 pages
- Size 254x178 mm
- Weight 680 g
- Language English 0
Categories
Short description:
These authors use soft computing techniques and fractal theory in a new approach to mathematical modeling, simulation, and control of complexion-linear dynamical systems. First, a fuzzy-fractal approach is presented with examples in the PROLOG programming language, followed by a fuzzy-genetic approach to automated simulation of dynamical systems il
MoreLong description:
These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming language. Second, a new fuzzy-genetic approach to automated simulation of dynamical systems is presented. It is illustrated with examples in the MATLAB programming language. Third, a new method for model-based adaptive control using a neuro-fussy fractal approach is combined with the methods mentioned above. This method is illustrated with MATLAB. Finally, applications of these new methods are presented, in the areas such as biochemical processes, robotic systems, manufacturing, food industry and chemical processes.
MoreTable of Contents:
Introduction to Modeling, Simulation and Control of Non-linear Dynamical Systems. Fuzzy Logic for Modeling Neural Networks for Control. Genetic Algorithms and Fractal Theory for Modeling and Simulation. Fuzzy-Fractal Approach for Adaptive Model-Based Control. Advanced Applications of Automated Modeling and Simulation. Advanced Applications of Automated Mathematical Modeling and Simulation. Advanced Applications of Adaptive Model-Based Control.
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