• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1115-1120
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• 2003

The present paper shows the possible development of particle swarm optimization (PSO) based fuzzy-neural networks (FNN) which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs which can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by an user. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. Hidden Markov Model (HMM) based automatic speech recognizers are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system is successfully employed in a real life situation for motion control of a redundant manipulator.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.370-372
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• 1994

In this paper, a stable adaptive composite control system consisting of a PID and a fuzzy controllers is designed to control nonlinear systems. In the fuzzy controller, parameters of membership functions characterizing the linguistic terms change according to some adaptive law. Also, parameters of PID controller change according to some adaptive law. These adaptive laws are based on the Lyapunov synthesis approach. Then, it is proved that the closed-loop system using such an adaptive composite control system is globally stable in the sense that all signals involved are bounded and the tracking error converges to zero. We apply this adaptive composite control system to control a nonlinear system.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.344-347
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• 2005

Helper T(Th) cells regulate immune response by producing various kinds of cytokines in response to antigen stimulation. The regulatory functions of Th cells are promoted by their differentiation into two distinct subsets, Th1 and Th2 cells. Th1 cells are involved in inducing cellular immune response by activating cytotoxic T cells. Th2 cells trigger B cells to produce antibodies, protective proteins used by the immune system to identify and neutralize foreign substances. Because cellular and humoral immune responses have quite different roles in protecting the host from foreign substances, Th cell differentiation is a crucial event in the immune response. The destiny of a naive Th cell is mainly controlled by cytokines such as IL-4, IL-12, and IFN-${\gamma}$. To understand the mechanism of Th cell differentiation, many mathematical models have been proposed. One of the most difficult problems in mathematical modeling is to find appropriate kinetic parameters needed to complete a model. However, it is relatively easy to get qualitative or linguistic knowledge of a model dynamics. To incorporate such knowledge into a model, we propose a novel approach, fuzzy continuous Petri nets extending traditional continuous Petri net by adding new types of places and transitions called fuzzy places and fuzzy transitions. This extension makes it possible to perform fuzzy inference with fuzzy places and fuzzy transitions acting as kinetic parameters and fuzzy inference systems between input and output places, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.4
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• pp.1-9
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• 2001

In this paper, we propose Fuzzy M/M/1/K queueing network model which has derived by appling the fuzzy set theory to M/M/l/K queueing network model in which has single server and system capacity K. When the arriving rate of input job and the servicing rate of a server arc represented as the linguistic attributes, the system analysis can be performed by using this model. The major evaluation measures of system such as the average number of jobs existing in the system, the average number of jobs into system, and the average spending time of job in system etc. are derived for the evaluation of system. Computer simulation was performed for verifying the effectiveness of these result equations. In which the various fuzzy arriving rates and fuzzy servicing rates according to varying the system capacity K were given for the system evaluation. We verified that the results of simulation are in accord with the expected evaluations in the proposed fuzzy model.

• Journal of Biomedical Engineering Research
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• v.14 no.4
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• pp.341-348
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• 1993

Automated system to analyze liver function test results is presented based on fuzzy logic knowledge. Clinician's knowledge and experience was first expressed in linguistic terms fol- lowed by conversion to numerical values to create membership functions of disease possibility for each test item and liver disease. Membership functions were then compensated for different relative importances of test items. Liver diseases considered were acute viral hepatitis (AVH), chronic persistent hepatitis(CPH), chronic active hepatitis(CAH), and liver cirrhosis(LC), Liver function test results of alanine aminotransferase(ALT), aspartate amino- transferase(AST) , glutamate dehydrogenase(GDH), ornithine carbamyltransferase(OCT) , ALT/AST, and 10* GDH/ALT in 218 patients were analyzed by the present system, welch resulted in 80% accuracy. AVH and CAH showed the highest 93 % and the lowest 58% ac- curacies, respectively, which was similar to the clinician's expectation. The simple mathemat- ical formulation of the present system would enable an easy implementation in commercial analysis instruments. Also, the identical fuzzy logic can be applied to similar diagnostic envi- ronments in general.

• Management & Information Systems Review
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• v.2
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• pp.311-334
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• 1998

By decreasing in CPU price and development of computer assembling technology, personal computer fake a good chance to accelerate its supply. Recently, as being introduced new computing technology so called multi media, teaming assist system which is based on single media such as studying book, cassette tape, video tape, or something else is rapidly being replaced by new assist education system based on multi media in which it is operated by the personal computer. In the computer assist education system, there is an evaluation module which appraise learner's study level into the next study strategy. At the view of this point, this part is very important. In this part, there are some factors like Importance, complexity, or difficulty which commonly include fuzzy factors in our surrounding. But until now, we are still out of the level to handle the evaluation module adequately among the some studies. In this study, we would like to suggest a new module that evaluate learning achievement of ICAI which have a variety of fuzzy environment. We combine Independent fuzzy environment like importance, complexity, difficulty into making total evaluation of learner's achievement. By the result, with expressing by linguistic form, this study can provide the theoretical basis in which we will be able to carry out sentence toward evaluation among elementary school.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.81-89
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• 1996

A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.143-163
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• 1997

Incident management is significant far the traffic management systems. The management of incidents determines the smoothness of freeway operations. The dynamic nature of incidents and the uncertainty associated with them require solutions based on the incident operator's judgment. Fuzz systems attempt to adapt such human expertise and are designed to replicate the decision making capability of on operator. Fuzzy systems process complex traffic information, and transmit it in a simplified, understandable form to human traffic operators. In this study, fuzzy rules were developed based on data from real incidents on Santa Monica Freeway in LosAngeles. The fuzzy rules ail linguistic based, and hence, user-friendly. A comparison of the results from the linguistic model with the real incident durations indicate that the outputs from the model reliably correspond to real incident durations conditions. The model reliably predicts the freeway incident duration. The modes can thus be used as an effective management tool for freeway incident response systems. The approach could be applied to other problems regarding dispatch systems in transportation.