Structure identification of fuzzy model
Fuzzy Sets and Systems
Stable adaptive systems
A bioreactor benchmark for adaptive network-based process control
Neural networks for control
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
Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system
Information Sciences—Informatics and Computer Science: An International Journal
Combining fuzzy, PID and regulation control for an autonomous mini-helicopter
Information Sciences: an International Journal
Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks
Information Sciences: an International Journal
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
Hi-index | 0.00 |
We describe in this paper a hybrid method for adaptive model-based control of non-linear dynamic systems using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic System Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic systems. 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.