Minkee Park

A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugenos model (1985), because it has the same structure as that of Takagi and Sugenos model. It is also as easy to implement as Sugeno and Yasukawas model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawas model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.

A transformed input-domain approach to fuzzy modeling

This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm.

A simply identified Sugeno-type fuzzy model via double clustering

Abstract Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1–4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

A new approach to the identification of a fuzzy model

Abstract This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

A new identification method for a fuzzy model

This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures are suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.<<ETX>>

Identification of Takagi-Sugeno fuzzy models via clustering and Hough transform

In this chapter we consider the identification of a Takagi-Sugeno fuzzy model (TS fuzzy model) [2]. This type of fuzzy model is especially useful in the area of fuzzy model-based control [10]. The TS fuzzy model is a nonlinear system model represented by fuzzy rules of the type

Tracking navigation using fuzzy inference and sonar-based obstacle avoidance

{R^i}:If{x_1}isA_1^iand...and{x_m}isA_m^ithen{y^i} = a_0^i + a_1^i{x_1} + ... + a_m^i{x_m}

Fuzzy model based environmental stiffness identification in stable force control of a robot manipulator

(1.1) where R i ( i = 1, 2, …,n denotes that the i-th fuzzy rule, x j (j = 1,2,…, m) are input variables and y i is an output. Furthermore, a j i are the parameters contained in the consequent (then-part) of the i-th rule, and the A 1 i ,A 2 i ,…,A m i are the linguistic values taken by the input variables in the antecedent (if-part) of the i-th rule. The meaning of these linguistic values is defined by corresponding membership functions. As shown in (1.1) and (1.2), this fuzzy model describes a nonlinear input-output relation.