• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1278-1282
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• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.39-39
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• 2003

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.515-524
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• 2007

This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.17-22
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• 2024

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.599-609
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.461-464
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• 2007

In this paper, we propose a fuzzy Kalman filtering to deal with a estimation error covariance. The T-S fuzzy model structure is further rearranged to give a set of linear model using standard Kalman filter theory. And then, to minimize the estimation error covariance, which is inferred using the fuzzy system. It can be used to find the exact Kalman gain. We utilize the genetic algorithm for optimizing fuzzy system. The proposed state estimator is demonstrated on a truck-trailer.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.350-355
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• 1997

The problem of mathematical model for an unknown system by measureing its input-output data pairs is generally referred to as state estimates. The state estimation problem is often of importance in its own right since we may want to know the value of the states. For instance, in navigation, we may take noisy positional fixes using satelite or radar navigation, and the estimator can use these measurements to provide accurate estimates of current position, hedaing, and velocity. And the state estimates can also be used for control purposes. Then it is very important to know the state of plant. In this paper, the theory of the minimization of a loss function was used to design the fuzzy system. Here, the used teory is Least Square Esimation method. This parametrization has the Linear in the parameters charcteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation m thod then performs as a nonlinear estimator. If several fuzzy label are defined for the input variables at the antecedent part, the fuzzy system then behaves as a collection of nonlinear estimators where different regions of rules have different parameters. In simulation results, the fuzzy model controlled a difference in the structure between the actual plant and the fuzzy estimator. It is also proved that the fuzzy system is equivalent to its transformed system. therefore we was able to get the state space equation of system with the estimated paramater.