• 전자공학회논문지C
• /
• 제36C권11호
• /
• pp.84-91
• /
• 1999

본 논문에서는 삼각함수와 양자화 된 삼각 퍼지함수를 가중퍼지평균(WFM: Weighted Fuzzy Mean)에 적용하여 비교하였다 또한 잡음의 특성에 따라서 영상에 포함된 잡음을 완전히 제거할 수 없는 단점을 개선하기 위하여, 계층적 구조의 결정기반 신경회로망(DBNN: Decision Based Neural Network)에 퍼지알고리즘을 적용하여서, 영상에 포함된 잡음을 제거하고 동시에 정보의 손실을 최소화하고 최적의 정보를 얻을 수 있는 고속 가중 퍼지결정 신경회로망(FDNN: fuzzy Decision Neural Network)을 구현하였다. 그리고 모의실험을 통하여 WFM과 FDNN의 성능을 비교하였으며, 보트(boats)의 영상에 대한 평균자승오차 (MSE:Mean Square Error)를 비교한 결과 제안된 FDNN이 우수함을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권2호
• /
• pp.71-76
• /
• 2011

The problem of decision making under imprecise environments are widely spread in real life decision situations. We present a method of object recognition from imprecise multi observer data, which extends the work of Roy and Maji [J Compu. Appl. Math. 203(2007) 412-418] to generalized intuitionistic fuzzy soft set theory. The method involves the construction of a comparison table from a generalized intuitionistic fuzzy soft set in a parametric sense for decision making.

• Journal of Power Electronics
• /
• 제2권4호
• /
• pp.305-311
• /
• 2002

In this paper, two approaches for designing tracking controllers for a variable reluctance motor (VRM), namely the Lyapunov-based fuzzy approach and the classical approach, are compared. The nonlinear model of a VRM is first addressed. The two control schemes are introduced afterwards, and then applied to obtain tracking controllers. Simulation results of a sample case, to which the methods are applied, are also presented. Comparison of the methods based on the results obtained concludes the paper.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.84-89
• /
• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제10권3호
• /
• pp.218-223
• /
• 2010

Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.68-68
• /
• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권8호
• /
• pp.551-560
• /
• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. 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 (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• 대한인간공학회:학술대회논문집
• /
• 대한인간공학회 1995년도 춘계학술대회논문집
• /
• pp.25-29
• /
• 1995

When we design a product, we need to consider human sensibility for the product. In this study, we developed a technique to measure human sensibility for a product. Because human sensibility for a product is very subjective and fuzzy, it is hard to measure easily. To deal with this difficulty effectively, we used fuzzy-weighted checklist to this problem. The fuzzy- weighted checklist presents a fuzzy version of the weighted checklist technique computerized for evaluating or comparing complex system (or subjects). In this technique, we used pairwise comparison to get the relative weights of wensibility factors. Also, we used linguistic ratings to get the scores of sensibility factors for a product. Then, we synthesize the scores of sensi- bility factors to get fuzzy composite score (and linguistic approximation). If there are several alternatives, we can conduct alternative comparison. Finally, we wrote the program of this technique by Quick Basic software. As an example, this technique applied to car. The results show that we can use this technique effectively to the quantitative evaluation of human sensibility

• 전자공학회논문지B
• /
• 제33B권4호
• /
• pp.191-200
• /
• 1996

This paper presetns a new fuzzy neural network for fuzzy modeling.The fuzzy neural network is composed of 4 layers and then odes of each layer represent the each step of the if-then fuzzy inference. A heuristic based on the back-propagation algorithm is proposed to ajdust the parameters of the fuzzy nerual network. We prove the feasibility of the network using the experiments on modeling a nonlinear mathematical system and the comparison with previous research.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제6권2호
• /
• pp.100-104
• /
• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.