• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.603-608
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• 2008

In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.95-106
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• 2000

The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.9-19
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• 2013

This paper deals with fuzzy $H_{\infty}$ filtering problem of discrete-time nonlinear Markovian jump systems with state and output time delays. The purpose is to design fuzzy $H_{\infty}$ filter such that the corresponding estimation error system with time delays and initial state uncertainties is stochastically stable and satisfies an $H_{\infty}$ performance level. A sufficient condition for the existence of fuzzy $H_{\infty}$ filter is given in terms of matrix inequalities. In order to relax conservatism, a stochastic mode dependent fuzzy Lyapunov function is employed. The Lyapunov function not only is dependent on the operation modes of system, but also includes the fuzzy membership functions. An illustrative example is finally given to show the applicability and effectiveness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.19-28
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• 2000

It is not so easy to control the wheeled mobile robot because of some causes like non-holonomic constraints. To overcome these problems, a controller that PD system is combined with fuzzy process is composed of several steps that have each separate algorithm and niche search algorithm and immune algorithm is applied partly. Output term set is changed by search that is performed to get optimal elements and then the rule base is also reformed. The fitness for the altered system is estimated and the surplus elements are removed. After the adjustment of output term set and rule base is finished, input and output membership functions is tuned.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.14-24
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• 1996

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation ation padptu sing genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusior~ or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to three examples of the classification of iris data, the discrimination of thyroid gland cancer cells and the recognition of confusing handwritten and printed numerals. In the recognition of confusing handwritten and printed numerals, each sample numeral is classified into one of the groups which are divided according to the sample structure. The fuzzy classifier proposed in this paper has recognition rates of 98. 67% for iris data, 98.25% for thyroid gland cancer cells and 96.3% for confusing handwritten and printed numeral!;.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1013-1018
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• 2007

In general, there are many sensors for fuel injection control such as an air flow sensor, an air intake temperature sensor, a cooling water temperature sensor, a throttle position sensor, and a motor position sensor. In this paper, we proposed a method for controlling the amount of fuel consumption in cars using fuzzy control technique by temperature change of an air intake temperature sensor and air-fuel ratio, the ratio of air and fuel mixture. In the proposed method, the amount of fuel injection is controlled by fuzzy membership functions and fuzzy inference rules established for air-fuel ratio, air intake temperature, and final fuel compensation, after computing air-fuel values using each amount of air intake and each amount of fuel injection. We verified that the proposed method is more efficient than conventional methods in fuel injection control from the results of the simulation program.