• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.97-98
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• 2008

본 논문에서는 다중 출력을 가지는 퍼지 관계 기반 퍼지뉴럴네트워크를 설계한다. 퍼지 관계 기반 퍼지뉴럴네트워크는 선체 인력 변수에 따른 입력 공간을 분할함으로서 네트워크를 구성한다. 규칙의 전반부는 앞서 언급한 전체 입력 공간을 분할하여 표현하고, 규칙의 후반부는 다항식으로서 표현되며 오류역전파 알고리즘을 이용하여 연결가중치인 후반부 다항식을 학습한다. 또한, 각 입력에 대만 전반부 멤버쉽함수의 정점과 학습률 및 모멤텀 계수를 유전자 알고리즘을 이용하여 최적 동조한다. 따라서 유전자 알고리즘을 이용하여 퍼지뉴럴네트워크를 최적 설계한다. 마지막으로 제안된 모델은 표준 모델로서 널리 사용되는 수치적인 예를 통하여 평가한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.343-345
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• 2016

본 논문에서는 아동이 그린 그림에 대해 ART2 알고리즘을 적용하여 색채 정보를 군집화하고, 군집화 된 색채 정보의 중심 벡터 값들을 퍼지화 한다. 퍼지화 된 색채 정보의 소속도를 퍼지 추론 규칙에 적용한 후에 비퍼지화 한다. 비퍼지화 된 결과를 적용하여 아동의 심리 상태를 분석한다. 제안된 방법을 실험하여 알슐러와 해트윅(Alschuler and Hattwick)의 색채에 따른 심리 상태와 비교한 결과, 제안된 심리 분석 방법이 알슐러와 해트윅의 색채에 따른 심리 상태 분석 결과와 거의 일치하는 것을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.34-42
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• 1997

This paper presents new fuzzy learning algorithms and their applications to time series prediction. During generating fuzzy rules from numerical data, there is a tendency to produce conflicting rules which have same premise but different consequence. To resolve the problem, we propose MCM(Modified Center Method) which is proven to reduce the error in the prediction. We have applied MCM to the analysis of Mackey-Glass time series and Gas Furnace da.ta to verify its efficiency.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.95-98
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• 2003

본 연구에서는 기존의 표면변형 디스플레이 방법의 단점을 해결하기 위한 새로운 방식의 퍼지 디스플레이 알고리즘을 제안한다. 제안한 방법에서 표면 변형을 디스플레이하기 위해서는 원래의 퍼지 모델에 단지 하나의 퍼지규칙을 추가함으로서 쉽고 간단하게 구현 할 수 있다. 제안된 퍼지 디스플레이 알고리즘은 Matlab과 OpenGL을 통한 시뮬레이션으로 물체의 표면변형을 디스플레이하기에 충분함을 검증할 수 있었다.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.13-20
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• 2006

In the advanced country, It is forecasting farm prices using intelligence style of farming technique. In our country, It is offering basis research to prevent the prices rising and falling, But, It is impossible that no one can predict exactly for farming price. In this paper to improve forecasting farming price using neural network as a preprocessing. Also, we developed a fuzzy algorithm for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results preyed reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.1-8
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• 2007

In recent, the issues of sensitivity and psychology of human have received much attention from researchers and practitioners. In this paper. we analyze the information of color and location in order to detect the sensitivity and psychology by means of human vision on color space organization in a presented picture. After this process, we propose a method to determine psychology states through the space organization by using a fuzzy membership function which can be used to analyze direction information for the sensitivity. The proposed method is applied to the psychology states based on the space organization of Alschuler and Hattcick's method and to the space organization of Gunnwald's method. As a result, we present that the proposed method is very similar to a pattern classification of Alschuler and Grunwald.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.603-608
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• 2008

In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.