• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1338-1343
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• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.885-892
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• 2004

In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.537-540
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• 2000

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.79-82
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• 2006

본 논문은 원자력 발전소의 주요 제어계통 중에서 터빈 조속기 제어계통에 관련한 성능평가를 목적으로 한다. 터빈 조속기 계통은 고압의 유압계통으로 구성되어 있어 구동설비가 복잡하다. 복잡한 기계설비는 운전 중 많은 오동작에 의한 고장을 일으키고, 유지보수에 어려움이 있다. 이러한 복잡한 기계설비에 있어 운전원에 의한 기계성능 평가는 불리한 점이 많다. 예를 들어 서로 다른 시간에서 일어나는 같은 상황에 대해 다른 판단을 내릴 수 있다는 점이다. 터빈 조속기 계통의 기계설비에 있어서 터빈 밸브 유압공급 및 구동장치는 각 터빈벨브 자체에 부착되어 있어 터빈벨브를 동작시킨다. 터빈벨브들은 구동기 유압 서보실린더(Actuator Hydraulic Servo Cylinder)에 의해 열리고 압축된 스프링에 의해 닫힌다. 이러한 시스템을 진단하기 위해서 본 논문에서는 밸브의 내부 압력의 특징정보를 입력으로 하는 퍼지이론을 적용하여 터빈 밸브 구동설비의 성능을 판단하고자 한다. 퍼지이론에 적용하기위해 터빈 조속기 제어계통의 고압 터빈 조절 벨브와 고압 터빈 정지 밸브의 압력변화 데이터를 이용한다. 퍼지이론의 적용과정에서 퍼지 Rule은 실제 운전원이 압력변화 데이터에 대한 판단기준을 근거로 하여 정하기로 한다. 그리고 퍼지이론에 적용한 결과를 분석하고 실제 터빈 조속기 계통의 전문가가 판단 결과와 비교하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.525-532
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• 2004

Telemanipulator is distingushed from industrial robot by iterating same specified work. Manipulator operator is included in control loop for controlling the telemanipulator because he decide directly during the work and order controllabily. We implement fuzzy controller for reducing the modelling error of telemanipulator which depend on the PID controller. But position-force control method of bidirectional control impose unsafety of vibiration and Analytic Hierchy method can stabilize for reducing nonlinear modelling error by expert operator because of transformation empirical control rule to linear model.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.77-86
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• 1995

The new architecture that fuzzy logic control(FLC) with difficulties for tuning membership function (MF) is parallel with neural networks(NN) to be learned from the output of FLC is proposed. Therefore proposed scheme has the characteristics to utilize the expert knowledge in design process, to be learned during the operation without any learning mode. In this architecture, the function of the FLC is to supply the sliding surface which is constructed on the phase plane by rule base for giving the desired control characteristics and learning criterion of NN and the stabilization of the control performance before NN is learned, The function of the NN is to let the system trajectory be tracked to the sliding surface and reached to the stable point.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.188-190
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• 2005

This paper is proposed HAI controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• KIEE International Transactions on Power Engineering
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• v.5A no.3
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• pp.260-268
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• 2005

This paper proposes an improved investment pnonty decision method of facilities considering the reliability of distribution networks. The proposed method decides an investment order of the facilities combining, by fuzzy rules, the investment priority decision by KEPCO and that by reliability evaluation indices. The reliability evaluation indices are SAIFI (System Average Interruption Frequency Index) and SAIDI (System Average Interruption Duration Index). The reliability analysis method of distribution networks applied in this paper utilizes the analytic method, where the used reliability data is the historical data of KEPCO. Particularly, we assumed that the failure rate increases as the equipment ages. To verify the performance of the proposed method, we applied it with the planned projects to reinforce the weak electrical facilities in KEPCO in 2004. The evaluation result showed that, under a limited budget, the reliability of KEPCO in the Busan region using the proposed method could be enhanced if used rather than the conventional method typically in place. Therefore, the results verify that the proposed method can be efficiently used in the actual priorities method for investing in the electrical facilities.