• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.2-102
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• 2002

In this paper, the improved Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C-Means) clustering method and optimization algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parame...

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.153-160
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• 1996

This paper presents a method that determines the stem profile dimensions for full, slow-speed ship using fuzzy modeling applied the genetic algorithm and compares with the database of ships.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.588-588
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• 1991

This paper proposes a compliance robot control algorithm using fuzzy adaptive controller and fuzzy compliance vector generator. In the compliance robot control, we need more adaptivity because the linear modeling in robot dynamics is getting more difficult by contact with external environment. Existing adapitive controllers have difficulty in realtime processing. So in order to overcome it, We adopt fuzzy adaptive controller and propose fuzzy compliance vector generator for flexible compliant motion. We analyze and confirm the proposed algorithm by surface processing experiment with a control system implemented by VME system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1098-1101
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• 1993

The purpose of this work is to build a fuzzy model of a batch culture for a process control. The process is highly nonlinear system with large delay. This paper presents two methods of modeling the process behavior. One is a method of recognizing them by fuzzy rules that are contracted by the pattern analysis in consideration of skilled operators' way. The other is a method of predicting them by approximate linear models and fuzzy rules by statistic analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.146-151
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• 1994

An empirical comparison of static fuzzy relational models which are identified with different fuzzy implication operators and inferred by different composition operators is made in case that all the information is represented by the fuzzy discretization. Four performance measures (integral of mean squared error, maximal error, fuzzy equality index and mean lack of sharpness) are adopted to evaluate and compare the quality of the fuzzy relational models both at the numerical level and logical level. As the results, the fuzzy implication operators useful in various fuzzy modeling problems are discussed and it is empirically shown that the selection of data pairs is another important factor for identifying the fuzzy model with high quality.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.