Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.

A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.

This paper describes two methods to construct fuzzy inference rules by the simplified fuzzy reasoning. The present methods have a construction mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create a rule unit near the selected rule which has the nearest position from the central input space for the central value. The second is to create a rule unit near the selected rule which has the minimum width for the width. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.

The abilities of fuzzy inference methods in modeling of complicated systems are implemented to electromagnetics for the first time. The very popular and well known monopole antenna is chosen as a general example and a fast, simple and accurate fuzzy model for its input impedance is made by introducing a new point of view to impedance basic parameters. It is established that a surprisingly little number of input data points is sufficient to make a full model and also the system behavior (dominant rules) are saved as simple membership functions. The validity of the derived rules is confirmed through applying them to the case of thin-angled monopole antenna and comparing the results with the measured. Finally using the spatial membership function context, input impedance of thick-angled monopole antenna is predicted and a novel view point to conventional electromagnetic parameters is discussed to generalize the modeling method.

In this paper, a clustering-based method is proposed for automatically constructing a multi-input Takagi-Sugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to provide a systematic procedure to partition the input space so that the number of fuzzy rules and the shapes of fuzzy sets in the premise part are determined from the given input-output data. In the parameter identification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-output data. Finally, two examples are used to illustrate the effectiveness of the proposed method.