• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.708-711
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• 1997

This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.191-194
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• 2002

In this paper, a new GA-based methodology with information granules is suggested for construction of the fuzzy classifier. We deal with the selection of the fuzzy region as well as two major classification problems-the feature selection and the pattern classification. The proposed method consists of three steps: the selection of the fuzzy region, the construction of the fuzzy sets, and the tuning of the fuzzy rules. The genetic algorithms (GAs) are applied to the development of the information granules so as to decide the satisfactory fuzzy regions. Finally, the GAs are also applied to the tuning procedure of the fuzzy rules in terms of the management of the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example-the classification of the Iris data, is provided.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.207-210
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• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2192-2195
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• 2002

Even though, tanks are used at the many industry plants, it is very difficult to control the tank level without any overflow and shortage; moreover, cause of its complication of dynamics and nonlinearity, it's impossible to realize the accurate control using the mathematical model which can be applied to the various operation modes. However, the sliding mode controller(SMC) is known as having the robust variable structures for the nonlinear control systems with the parametric perturbations and with the sudden disturbances. It's difficult to find SMC's parameters, and SMC is bring chattering which injures actuator and increases error. In this paper, Genetic Aloglism based Fuzzy Sliding Mode Controller(GA-FSMC) for the precise control of the coupled tank level was proposed. Genetic Algolism and Fuzzy logic are adapted to find SMC's parameters and reduce the chattering. The simulation result is shown that the tank level could be satisfactorily controlled with less overshoot and steady-state error by the proposed GA-FSMC.

• Korean Journal of Cognitive Science
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• v.6 no.3
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• pp.25-40
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• 1995

This paper proposes a generating method of fuzzy rules by genetic and descent method (GAGDM),and its applied to classification problems.The number of inference rules and the shapes of membership function in the antecedent part are detemined by applying the genetic algorithm,and the real numbers of the consequent parts are derived by using the descent method.The aim of the proposed method is to generation a minmun set of fuzzy rules that can correctly classify all training patterns,and fiteness function of GA defined by the aim of th proposed method.Finally,in order to demonstrate the effectiveness of the present method,simulation results are shown.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.118-125
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• 2003

This paper presents a scheme for the placement of dispersed generator systems(DGs) based on load model in unbalanced systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm The method proposed is applied to IEEE 13 bus unbalanced distribution systems to demonstrate its effectiveness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.178-185
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• 2013

As computer technology continues to develop, computer networks are now widely used. As a result, there are many new intrusion types appearing and information security is becoming increasingly important. Although there are many kinds of intrusion detection systems deployed to protect our modern networks, we are constantly hearing reports of hackers causing major disruptions. Since existing technologies all have some disadvantages, we utilize algorithms, such as the fuzzy C-means (FCM) and the support vector machine (SVM) algorithms to improve these technologies. Using these two algorithms alone has some disadvantages leading to a low classification accuracy rate. In the case of FCM, self-adaptability is weak, and the algorithm is sensitive to the initial value, vulnerable to the impact of noise and isolated points, and can easily converge to local extrema among other defects. These weaknesses may yield an unsatisfactory detection result with a low detection rate. We use a genetic algorithm (GA) to help resolve these problems. Our experimental results show that the combined GA and FCM algorithm's accuracy rate is approximately 30% higher than that of the standard FCM thereby demonstrating that our approach is substantially more effective.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.