• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.2
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• pp.62-69
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• 2000

In this paper, we design a GA-fuzzy precompensated PID controller for the load frequency control of two-area interconnected power system. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic-based precompensation approach for PID controller. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PID controller. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PID control and a fuzzy precompensated PID control in dynamic responses about the load disturbances of power system and is convinced robustness reliableness in view of structure.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.159-173
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• 2004

This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.153-156
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• 2001

Fuzzy controllers also show good performance In case of the systems being nonlinear and difficult to solve. But these fuzzy controllers have problems which have to decide suitable rules and membership functions. In general we decide those using the heuristic methods or the experience of experts. Therefore, many researchers have applied genetic algorithms to make fuzzy rule automatically. In this paper, we suggest a new coding method and a new crossover method to maintain the good fuzzy rule base and the shape of membership

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.607-612
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• 1998

In this paper, a controller using fuzzy-genetic algorithms is proposed for pat-tracking of WMR. A fuzzy controller is implemented so as to adjust appropriate crossover rate and mutation rate. A genetic algorithms is also implemented to have adaptive adjustment of control gain during optimizing process. To check effectiveness of this algorithms, computer simulation is applied.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.569-572
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• 2000

DEA has been effectively applied to various areas which need the evaluation of relative efficiency. We propose a DEA model based on fuzzy LP combined with Genetic Algorithm in order to consider uncertain synergy effects due to M&A of existing organization. We apply the suggested approach to forecasting the efficiency of merged academic departments in a university in Korea. We expect that our approach can be utilized to effectively realign existing departments.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.863-868
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• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1025-1028
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• 2000

In this paper, we apply to a genetic algorithm for fuzzy clustering. We propose initialization procedure and genetic operators such as selection, crossover and mutation, which are suitable for solving the problems. To illustrate the effectiveness of the proposed algorithm, we solve the manufacturing cell formation problem and present computational comparisons to generalized Fuzzy c-Means algorithm.