Vector in advanced neural network and fuzzy system.define term
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Abstract - 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.
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.
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