Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal TheoryThese 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 la |
Contents
INTRODUCTION TO MODELLING SIMULATION AND CONTROL OF NONLINEAR DYNAMICAL SYSTEMS | 1 |
FUZZY LOGIC FOR MODELLING | 9 |
NEURAL NETWORKS FOR CONTROL | 29 |
GENETIC ALGORITHMS AND FRACTAL THEORY FOR MODELLING AND SIMULATION | 65 |
FUZZYFRACTAL APPROACH FOR AUTOMATED MATHEMATICAL MODELLING | 81 |
FUZZYGENETIC APPROACH FOR AUTOMATED SIMULATION | 97 |
NEUROFUZZY APPROACH FOR ADAPTIVE MODELBASED CONTROL | 113 |
ADVANCED APPLICATIONS OF AUTOMATED MATHEMATICAL MODELLING AND SIMULATION | 127 |
ADVANCED APPLICATIONS OF ADAPTIVE MODELBASED CONTROL | 175 |
PROTOTYPE INTELLIGENT SYSTEMS FOR AUTOMATED MATHEMATICAL MODELLING | 225 |
PROTOTYPE INTELLIGENT SYSTEMS FOR AUTOMATED SIMULATION | 235 |
PROTOTYPE INTELLIGENT SYSTEMS FOR ADAPTIVE MODELBASED CONTROL | 242 |
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Other editions - View all
Modelling, Simulation and Control of Non-linear Dynamical Systems: An ... Patricia Melin,Oscar Castillo No preview available - 2001 |
Modelling, Simulation and Control of Non-linear Dynamical Systems: An ... Patricia Melin,Oscar Castillo No preview available - 2001 |
Common terms and phrases
adaptive control adaptive model-based control airplane ANFIS application approach automated mathematical modelling automated modelling automated simulation backpropagation bacteria base for model behavior identification biochemical reactors Castillo & Melin complex dynamical computer program consider control of non-linear crossover cycle of period defined defuzzification differential equations dynamic behavior error food production fractal dimension Fractal Theory Fractaldim function approximation fuzzy inference system fuzzy logic fuzzy model fuzzy rule base fuzzy set genetic algorithms identification and control input international trade layer linear Lyapunov exponents membership functions method for adaptive method for automated model selection modelling and simulation module network for control network for identification neural network neuro-fuzzy node non-linear dynamical systems numerical simulation obtained Oscar Castillo output parameter values periodic_part PROLOG programming language prototype intelligent system robot arm robotic dynamic systems robotic system shown in Figure simulated annealing Simulation and Control simulation results specific Sugeno system for automated techniques time_series variables vector