• 한국지능시스템학회논문지
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• 제2권2호
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• pp.42-60
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• 1992

An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

• 한국지능시스템학회논문지
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• 제3권2호
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• pp.58-67
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• 1993

Instead of Cartesian product in combining multiple input variables for fuzzy logic controllers, a fuzzy controller using fuzzy relations in inference procedure is proposed. Moreover, a technique is proposed by which conventional fuzzy control rules are transformed into the forms including fuzzy relations. It will be shown through several examples that the proposed technique gives smoother interpolation than conventional ones.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.895-898
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• 1993

Instead of Cartesian product for in combining multiple inputs for fuzzy logic controllers, a method using fuzzy relation in inference is proposed. Moreover, fuzzy control rule described by fuzzy relations is derived from given conventional fuzzy control rule by fitting concept. It will be shown through several examples that the proposed technique gives smoother interpolation than conventional ones.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.380-383
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• 2003

In this paper, we propose a fuzzy group decision making method for multiple decision maker-multiple objective programming problems to obtain the agreeable solution. In the proposed method, considering the vague nature of human subjective judgement it is assumed that each of multiple decision makers has a fuzzy goal for each of his/her own objective functions. After eliciting the membership functions from the decision makers for their fuzzy goals, total M-Pareto optimal solution concept is defined in membership spaces in order to deal with multiple decision maker-multiple objective programming problems. For generating a candidate of the agreeable solution which is total M-Pareto optimal, the extended weighted minimax problem is formulated and solved for some weighting vector which is specified by the decision makers in their subjective manner, Given the total M-Pareto optimal solution, each of the derision makers must either be satisfied with the current values of the membership functions, or update his/her weighting vector, However, in general, it seems to be very difficult to find the agreeable solution with which all of the decision makers are satisfied perfectly because of the conflicts between their membership functions. In the proposed method, each of the decision makers is requested to estimate the degree of satisfaction for the candidate of the agreeable solution. Using the estimated values or satisfaction of each of the decision makers, the core concept is desnfied, which is a set of undominated candidates. The interactive algorithm is developed to obtain the agreeable solution which satisfies core conditions.

• Smart Structures and Systems
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• 제16권6호
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• Smart Structures and Systems
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• 제31권1호
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.559-566
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• 2006

In the real system application, the interacting multiple model (IMM) based algorithm requires less computing resources as well as a good performance with respect to the various target maneuverings. And it further requires an easy design procedure in terms of its structures and parameters. To solve these problems, a fuzzy interacting multiple model (FIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as inputs of a fuzzy decision maker, is proposed. To verify the performance of the proposed algorithm, airborne target tracking is performed. Simulation results show that the FIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• International Journal of Control, Automation, and Systems
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• 제5권3호
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• 전기학회논문지
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• 제56권2호
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• pp.407-414
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• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.