• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.689-693
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• 2000

In this paper, we proposed a decoupled adaptive fuzzy sliding-mode control scheme in designing the SMC of a class of fourth-order nonlinear systems. These systems are decoupled the whole system into two second-order systems such that each subsystem has a separate control target expressed in terms of a sliding surface. Then, information from the secondary target conditions the main target, which, in turn, generates a control action to make both subsystem move toward their sliding surface. respectively, and Two sets of fuzzy rule bases are utilized to represent the equivalent control input with unknown system functions of the main target, The membership functions of the THEN-part. which is used to construct a suitable equivalent control of SMC. are changed according to adaptive law, Under this design scheme, we not only maintain the distribution of membership functions over state space but also reduce considerably computing time, we apply the decoupled adaptive sliding-mode control to control a nonlinear inverted pendulum system and confirms the validity of the proposed approach.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.19-26
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• 2012

Reversible lanes in urban intersections is used to efficiently control vehicles, reduce traffic congestion and increase the capacity of a roadway. But by far traffic control systems in urban intersections are simple and manually operated by police officers. In this study, we present a hybrid algorithm that intelligently resolve the moving direction of reversible lanes to efficiently manage the flow of traffic at intersection. The proposed algorithm consists of three stages:(i) fuzzy inference method to get the efficiency of moving direction, (ii) a provisional decision whether to change the reversible lane to different direction, (iii) a final evaluation criterion for changing the directions of the reversible lanes. The fuzzy inference results of efficiency are shown by using matlab application.

• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.55-61
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• 1998

The learning order of teaching materials to be a learning data in CAI is arranged from an easy item to a difficult one A learning in not necessary to be learned arranged this order. Actually the learning is done by the rules of trial and error on the sequences of an arrangement among items. In this papers, the constructed is modelled by the fuzzy inference after leaning the understanding on items by the intelligent CAI through the rile of trial and error of fuzziness. Given the difference of leaning and understanding, the leaning model is quantified by the order relationship among items and by the rules of fuzzy inference. The rule of trial and error of learning is restricted to the treatment of CAL system minimizing the rules of inference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.397-406
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• 2000

Genetic algorithm is used as a means of search, optimization md machine learning, its structure is simple but it is applied to various areas. And it is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an action base system evolving by itself is also being considered. There is to have a problem that depended entirely on heuristic knowledge of expert forming membership function and control rule for fuzzy controller design. In this paper, for forming the fuzzy control to perform self-organization, we tuned the membership function to the most optimal using a genetic algorithm(GA) and improved the control efficiency by the self-correction and generation of control rules.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.60-70
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• 1999

In this paper, model reference adaptive fuzzy controller (MRAFC) was proposed in order to overcome the difficulty of extracting rules and defects of the adaptation performance in the FLC. MRAFC comprised inner feedback loop consisting of the FLC and plant, and outer loop consisting of an adaptation mechanism which was designed for tuning a control rule of the FLC. A reference-model was used for design criteria of a fuzzy controller which characterizes and quantizes the control performance required in the overall control system. Tuning control rules of FLC is performed by the adaptation mechanism. The performance of proposed algorithm was verified through experiment for the DC servo motor.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.399-402
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• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a FNN(Fuzzy Neural-Network) control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. FNN has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.107-118
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• 1993

Continuous increasing of impervious area due to urbanization and rainfall quantity due to environmental changes aggravate flooding risk in low land area. Therefore. Seoul municipal authorities go on securing an ample budget for reinforcement and establishment of inner water and inland drainage pumping facilities. But. there is no investment for developing optimal operation rules for appropriate application of existing facilities. In this study. fuzzy control techniques are developed. and applied to 57 stations of inner water and inland drainage pump for model assessment. In these results. fuzzy models have more efficiency in the inland flooding protection than the existing pump operation rule by water level in the same conditions.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.39-43
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• 2002

In the frequency response analysis using a modal method, it is very important to determine the number of modes involved with the formulation of a frequency response function. Most engineers are inclined to determine mode truncation with their experience. But it is difficult for non-experts to decide the mode truncation reasonably in many problems of dynamic analyses. In this study, fuzzy theory is used to standardize the empirical determination of mode truncation so that not only the experts but also non-experts can decide a Proper mode truncation easily. Fuzzy rule base is based on the simulation results using finite element method. Numerical simulations show that the developed mode truncation method is a very effective method to choose the number of the considered modes.