**5. Conclusions**

438 Fuzzy Inference System – Theory and Applications

Fig. 9. Comparison of simulation results. (a) Outputs of the nonlinear system (solid line) and the identification model using the proposed network (dotted line) for the scheme 1 method. (b) Identification error of the approximated model for the scheme 1 method. (c) Outputs of the nonlinear system (solid line) and the identification model using the proposed network (dotted line) for the scheme 2 method. (d) Identification error of the approximated model for the

Fig. 10. Learning curves for the scheme 1 and scheme 2 parameter learning methods.

scheme 2 method.

In this paper, starting from the discussion of traditional CMAC approach, two novel and latest developed fuzzy CMACs are reviewed. By summarizing the drawbacks of the CMAC model, relative improvement made in the literature have been addressed and presented. Via the exhibited self-constructing FCMAC (SC-FCMAC) and parametric FCMAC (P-FCMAC), not only the inference ability of FCMAC is demonstrated, but also presented the state-of-the art in the field of fuzzy inference systems.
