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This paper introduces a new fuzzy modeling of an unknown system. The heart of the proposed modeling is fuzzy interpolation involving resolution reduction that generates two different types of information to define single-input, single-output subsystems for an unknown system. Input is identified using a heuristic based on the proposed technique. System behavior is defined as a curve corresponding to individual input. These curves are found using fuzzy curve fitting applied to data points. Behavior curves are saved one of three ways. When the first is geometrical, the second and third use a neural network as a linear prediction coder. Convergence of the linear prediction coder specifies the membership function domain. Inference rules are extracted by observing the exactness of single-input behavior using a fuzzy method. The combination rule is new and combines single-input, single-output behavior to obtain the system model. Our proposal cancels noise well, recognizes actual input, and expresses complicated nonlinear systems with a very small number of rules.

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Auto exposure (AE) is an important function ofvideo cameras to adjust the image luminance. I n this paper, an exposure control system of the A E using color information is discussed. Curren,t A E systems detect special image conditions such as backlighting and excessive frontlighting in which the luminance of a main object deteriorates, and compensate the exposure in order to obtain the appropriate luminance of the main object.The compensation of backlighting / excessive frontlighting as characterized by adjusting luminance of the main object where it is appropriate, causing the background to become worse. However, in A E systems th)at h,ave been proposed so far, the compensation amounts are determined according to the degree of backlighting and excessive frontlighting, regardless of importance of the background. The exposure control system proposed an this paper uses "hue" and "chroma" of pixels to derive the importance of the background, and determines a compensation amount by fuzzy reasoning. Simulations of A E are carried out in conventionad systems and the proposed one. The performance of each system is tested through assessment experiments of human subjects for image samples of simulation results, and the proposed system is shown to be eficient for AE.

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