• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2004.10a
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• pp.171-180
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• 2004

Underground mining causes surface subsidence long after the mining operation had been ceased. Surface subsidence can be in the form of saucer-shaped depression or collapsed chimneys or sinkholes. Sinkhole formations are predominant over shallow-depth room and pillar mines having weak overburden strata. In this study, occurrences of sinkholes due to mining activity are assessed based on local geological conditions and mining parameters using fuzzy reasoning techniques. All input and output parameters are represented with linguistic hedges. Numerous fuzzy rules are developed to relate sinkhole occurrences with input parameters using fuzzy relational matrix. Based on the combined fuzzy rules, possibility of sinkhole occurrences can be ascertained once the geological and mining parameters of any area are known.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.94-99
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• 1993

In this paper, the method for navigation and obstacle avoidance of an autonomous mobile robot is proposed. It is based on the fuzzy inference system which enables to deal with imprecise and uncertain information, and on the neural network which enables to learn input and output pattern data obtained from ultrasonic sensors. For autonomous navigation, the wall-following navigation utilizing input and output data by an expert's control action is constructed. An approach by the neural network is developed for the obstacle avoidance because of the redundant input data. For an autonomous navigation, the fuzzy control and the control of the neural network are integrated and its feasibility is demonstrated by means of experiment.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.185-188
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• 2001

In this paper, a cascade fuzzy control method is proposed, which presents a robustness of the effect of disturbances, and in which the number of rules of the controller increase linearly instead of exponentially with the number of input variables. The proposed fuzzy control method is composed of cascade structure. Each stage has a state and a change of state, and one consequent control variable, and previous consequent control variable is an input of next stage. Simulation of the proposed controller, which is applied to the linear and nonlinear system as SISO (single-input single-output) system, showed that the proposed control method has a good control performance and robustness of disturbances compared with a conventional fuzzy control method.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.337-339
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• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.86-93
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• 2006

The TAM(Topographic Attentive Mapping) network based on a biologically-motivated neural network model is an especially effective one for pattern analysis. It is composed of of input layer, category layer, and output layer. Fuzzy rule, for input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of core competency model for subcontractors of construction companies and show its usefulness.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.20-27
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• 1993

The white-balance system for color tevision is characterized by 5 input-5 output nonlinear process. A design strategy of fuzzy control rules is treated in which it can be adopted to the white balance adjustment for color television. A fuzzy rule based on an expert's knowledge is constructed, and then a multivariable fuzzy control rule is designed. Since human has just two hands, he can manipulate two variables simutaneously. In case when the process to be controlled has more than three control variables, expert's control rule is much different from the multivariable control rule. A multivariable fuzzy control rule is constructed by utilizing the expert' knowledge and rough relations between input and output variables, and its usefulness is shown by experiments.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.100-112
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• 1992

In this paper, an integrated document input/output system is developed which constructs the graphic document from a text file, converts the document into encoded facsimile data, and also recognizes printed/handwritten alphanumerics and Korean characters in a facsimile or graphic document. For an output system, we develop the method which generates bit-map patterns from the document consisting of the KSC5601 and ASCII codes. The binary graphic image, if necessary, is encoded by the G3 coding scheme for facsimile transmission. For a user friendly input system for documents consisting of alphanumerics and Korean characters obtained from a facsimile or scanner, we propose a document recognition algirithm utilizing several special features(partial projection, cross point, and distance features) and the membership function of the fuzzy set theory. In summary, we develop an integrated document input/output system and its performance is demonstrated via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.189-192
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• 1996

This paper presents a systematic approach to identify a linguistic fuzzy model for a multi-input and single-output complex system. Such a model is composed of fuzzy rules, and its output is inferred by the simplified reasoning. The structure and membership function parameters for a fuzzy model are automatically and simultaneously identified by GA (Genetic Algorithm). After GA search, optimal parameters for the fuzzy model are finely tuned by a gradient method. A numerical example is provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce the linguistic fuzzy model with higher accuracy and a smaller number of rules than the ones achieved previously in other methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.732-735
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• 2007

Recently the control of induction motor for position control has been extensively studied. The representative method is PIDA controller proposed by Jung&Dorf. By designed PIDA controller' parameter had large value. Moreover, this method is very analyze, so that, not adapted controller parameter in disturbance. Besides using generalize fuzzy controller. Because input and output membership function is linguistic type, therefore system response is very slow. So, in this paper we used optimized fuzzy controller. Optimized fuzzy controller is output membership function is unity value. The controller performance was estimated applied to induction motor' position control.