• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.370-370
• /
• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Journal of Electrical Engineering and Technology
• /
• 제11권2호
• /
• pp.309-319
• /
• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• 한국지능시스템학회논문지
• /
• 제7권2호
• /
• pp.96-105
• /
• 1997

본 논문에서는 복잡한 비선형 시스템의 모델동정을 위해 퍼지모델링의 새로운 방법이 제안된다. 제안된 FPNN모델링은 공정시스템의 입출력 데이터로부터 GMDH방법과 퍼지구현규칙을 이용하여 시스템의 구조와 파라미터 동정을 구현한다. 퍼지구현규칙의 전반부 구조와 파라미터 동정을 위하여 GMDH 방법과 희귀다항식 퍼지추론 방법이 사용되고 최적 후반부 파라미터 동정을 위하여 최소자승법이 사용된다. 가스로 시계열데이타 및 하수처리시스템의 활성화의 공정 데이터가 제안한 FPNN 모델링의 성능을 평가하기 위해 상용된다. 제안된 방법이 기존의 다른 논문과 비교하여 더 높은 정확도를 가진 지능형 모델을 생성함을 보인다.

• 한국정밀공학회지
• /
• 제27권2호
• /
• pp.60-69
• /
• 2010

Lookup-Table (LUT) based fuzzy controller for obstacle avoidance enhances operations faster in multiple obstacles environment. An LUT based fuzzy controller with Positive/Negative (P/N) fuzzy rule base consisting of 18 rules was introduced in our paper$^1$ and this paper shows a 50-rule P/N fuzzy controller for enhancing performance in obstacle avoidance. As a rule, the more rules are necessary, the more buffers are required. This paper suggests LUT sharing method in order to reduce LUT buffer size without significant degradation of performance. The LUT sharing method makes buffer size independent of the whole fuzzy system's complexity. Simulation using MSRDS(MicroSoft Robotics Developer Studio) evaluates the proposed method, and in order to investigate its performance, experiments are carried out to Pioneer P3-DX in the LabVIEW environment. The simulation and experiments show little difference between the fully valued LUT-based method and the LUT sharing method in operation times. On the other hand, LUT sharing method reduced its buffer size by about 95% of full valued LUT-based design.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.1430-1434
• /
• 2003

In this paper, for the fuzzy-reaching law method which has the characteristic of elimination of chattering at sliding mode as well as the characteristic of fast response at the design of variable structure controller with reaching law, optimal solutions for the determination of parameters of fuzzy membership functions by using genetic algorithm are proposed. Generally, the design of fuzzy controller has difficulties in determining the parameters of fuzzy membership functions by using a tedious trial-and-error process. To overcome these difficulties, this paper develops genetic algorithm of an optimal searching method based on genetic operation, and to verify the validity of this proposed method it is simulated through 2 link robot manipulator.

• 한국정밀공학회지
• /
• 제19권11호
• /
• pp.167-173
• /
• 2002

The look up table method conventionally applied to control the inner temperature and humidity of a laver drying machine has repeatedly occurred not only laver's damage but also inferior goods since the reaching time at the optimum state takes a long time. In this paper, a fuzzy control theory instead of the look up table was proposed to reduce the reaching time at the optimum state. The proposed method used six input variables and four output variables for the fuzzy control, and a triangle rule for a fuzzifier, The Mandani's min-max method was applied to a fuzzy inference. Also, the mean method of maximum was applied to a defuzzifier. The method applied to the fuzzy controller contributed to reduce the reaching time at the optimum state, and to minimize not only laver's damage but also inferior goods.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 D
• /
• pp.2314-2316
• /
• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• Structural Engineering and Mechanics
• /
• 제6권1호
• /
• pp.17-30
• /
• 1998

The method of pairwise comparison inherently contains information of ambiguity, fuzziness and conflict in design goals for a multiobjective structural design. This paper applies the principle of paired comparison so that the vaguely formulated problem can be modified and a set of numerically acceptable weight would reflect the relatively important degree of multiple objectives. This paper also presents a fuzzy global criterion method ($FGCM_{\lambda}$) included fuzzy constraints that coupled with the objective weighting rank obtained from the modified pairwise comparisons for fuzzy multiobjective optimization problems. Descriptions in sequence of this combined method and problem solving experiences are given in the current article. Multiobjective design examples of truss and mechanical spring structures illustrate this optimization process containing the revising judgement techniques.

• 제어로봇시스템학회논문지
• /
• 제8권11호
• /
• pp.941-947
• /
• 2002

Two methods of constructing T-S fuzzy model which is equivalent to a given nonlinear system are presented. The first method is to obtain an equivalent T-S fuzzy model by using the sum of linearly independent scalar functions with constant real matrix coefficients. The sum of products of linearly independent scalar functions is used in the second method. The former method is to formulate the procedures of T-S fuzzy modeling dealt in many examples of previous publications; the latter is a new method. By comparing the number of linearly independent functions used in the two methods, we can easily find out which method makes fewer rules than the other. The nonlinear dynamics of an inverted Pendulum on a cart is used as an equivalent T-5 fuzzy modeling example.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.601-606
• /
• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.