• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.219-232
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• 1998

A lot of difficulties exist in analyzing the structure of a system owing to the complex and organic relations in the systems we face in reality. Focuses have been put on the research of optimal solution in a defined structure, however, on the assumption that the structure of the system has been already defined. With the grasping of the structure as the most prior condition, ISM(Interpretive Structural Modeling) and FSM(Fuzzy Structural Modeling) are suggested as solutions in this paper. ISM uses the systematic application of some elementary notions of graph theory and boolean algebra, FSM uses Fuzzy conception for representing relationship between elements. In FSM, the entries in the relation matrix are taken to value on the interval [0,1] by virtue of a fuzzy binary relation. Numeric examples are used as the actual application as follows.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.516-518
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• 1995

This paper presents the optimal short-term operation scheduling by using fuzzy linear programming method on cogeneration systems connected with auxiliary equipments. Simulation is performed in case of the bottomming cycle. Modeling of cogeneration systems and auxiliary equipments is done, the effectiveness of modeling is evaluated through the detailed simulation. After the optimal operation scheduling is established by using linear programming method, the last optimal operation scheduling is established by using fuzzy linear programming method. The results of simulation show the auxiliary equipments can be effeciently operated in case of the bottomming cycle by modeling proposed in this paper.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.4
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• pp.1-8
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• 1995

This paper is presented determination of the hull form factors about a high speed coastal fishing boat using Fuzzy modeling. That is, the estimation curves of total resistance & EHP(Efficiency Horse Power) are induced by Fuzzy modeling algorithm from data of accumulated hull form factors and are compared with the results of model test. Also, the estimation curves of total resistance & EHP are utilized efficiently in determination the hull form factors about a high speed coastal fishing boat.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.688-694
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• 2005

In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.922-927
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• 1991

This study is aimed to investigate a design problem of the fuzzy logic controller for the activated sludge process(ASP) in sewage treatment. The modeling technique proposed by Sugeno is used to express the ASP effectively and identification of a fuzzy model of the ASP is carried out utilizing actual operational data obtained from a metropolitan sewage plants. The model-based fuzzy controller is designed by rules generated from the identified ASP fuzzy model. Feasibility of the designed controller is tested through computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.417-422
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• 2000

In this paper, an efficient fuzzy rule generation scheme for adaptive neuro-fuzzy system modeling using the Independent Component Analysis(ICA) as a preprocessing is proposed. Correlation between inputs was not considered in the conventional neuro- fuzzy modeling schemes, such that enormous number of rules and large amount of error were unavoidable. The correlation between inputs is weakened by employing ICA so that the number of rules and the amount of error are reduced. In simulation, the Box-Jenkins furnace data is used to verify the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.141-144
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• 2003

A composite adaptive dual fuzzy controller combining the approximate mathematical model, linguistic model description, linguistic control rules and identification modeling error into a single adaptive fuzzy controller is developed for a nonlinear system. It ensures the system output tracks the desired reference value and excites the plant sufficiently for accelerating the parameter estimation process so that the control performances are greatly improved. Using the Lyapunov synthesis approach, proposed controller is analyzed and simulation results verify the effectiveness of the proposed control algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.