Structure identification of fuzzy model
Fuzzy Sets and Systems
Stable adaptive systems
Neural networks for control
Numerical analysis and graphic visualization with MATLAB
Numerical analysis and graphic visualization with MATLAB
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Systems Analysis Modelling Simulation
Control Engineering Solutions: A Practical Approach
Control Engineering Solutions: A Practical Approach
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Computers & Mathematics with Applications
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Artificial neural network control of a heat exchanger in a closed flow air circuit
Applied Soft Computing
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Comparative study of type-1 and type-2 fuzzy systems for the three-tank water control problem
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.