Neural Networks and Fuzzy Systems for Nonlinear Applications

Nonlinear processes are difficult to control because there can be so many variations of the nonlinear behavior. The issue becomes more complicated if a nonlinear characteristic of the system changes with time and there is a need for an adaptive change of the nonlinear behavior. These adaptive systems are best handled with methods of computational intelligence such as neural networks and fuzzy systems. The problem is that development of neural or fuzzy systems is not trivial. Advantages and disadvantages of fuzzy systems will be presented and compared, including Mamdani, Takagi-Sugeno and other approaches. In the conclusion, advantages and disadvantages of neural and fuzzy approaches are discussed with a reference to their hardware implementation.