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Fuzzy rules (IF-THEN) that connect the input memberships with the output membership are then suggested [27].
The characteristics of the six models are given in Table 1: Model Characteristics Table 1 Model characteristics Model Name Input Memberships Output memberships Gauss/Const Gaussmf Const Gauss/Linear Gaussmf Linear Gauss2/Const Gauss2mf Const Gauss2/Linear Gauss2mf Linear Psigmf/Const Psigmf Const Psigmf/Linear Psigmf Linear.
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It consists of seven symmetric triangular input membership functions and output membership functions each.
Further, input membership function are tuned on-line to improve the time-optimal output.
Hence, a new set of input membership functions are generated while discarding the initial ones.
The input membership function is tuned on-line to improve the time-optimal output.
The work presented here demonstrates that tuning only the input membership function is sufficient and simpler than tuning both input and output membership functions in the standard FLC.
This paper describes the design of an adaptive fuzzy controller using iterative learning to tune input membership functions and scaling factor(s).
The proposed model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the curse of dimensionality problem.
(a) Input membership functions low and high of the DRC.
For simplicity, the selected input membership functions are trapezoidal.
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