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Fuzzy Adaptive filter. A Technical paper submitted By Malaya Kumar Meher Roll no :-EI200118349 NIST, Berhampur. An adaptive filter provides for an inherent closed loop to adjust signal quality for different cable plants and environmental conditions.
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Fuzzy Adaptive filter A Technical paper submitted By Malaya Kumar Meher Roll no :-EI200118349 NIST, Berhampur
An adaptive filter provides for an inherent closed loop to adjust signal quality for different cable plants and environmental conditions. Linear techniques fail is in image processing where conventional techniques cannot cope with the nonlinearities of the image formation model and do not take into account the nonlinear nature of the human visual system. Fuzzy logic is used for the nonlinear image processing, where conventional techniques fail. Introduction
Adaptive filter • Adaptive filters are used in various areas where the statistical knowledge of the signals to be filtered/analyzed are not known a priory or the signals may be slowly time variant. • The system tries to reduce the impact of the noise in the primary input exploring the correlation between the two noise signals. • This is equivalent to the minimization of the mean-square error E[e2(n)]
Block diagram of adaptive filter e(n) = s(n) + n2(n) – n3(n)……………….…(1) E[e2(n)] = E[s2(n)] + E[n2(n) – n3(n)]…….(2)
A fuzzy system is a nonlinear system formed by a set of fuzzy rules. Example of fuzzy rules Rule 1. If (u1; F1;1) AND : : :AND (uN; FN;1) THEN (v0;G1) Rule 2. If (u1; F1;2) AND : : :AND (uN; FN;2) THEN (v0;G2) : : : Rule M. If (u1; F1;M) AND : : :AND (uN; FN;M) THEN (v0;GM) Continue……….
Fuzzy logic adaptive controller (FLAC) is to detect the bias of measurements and prevent divergence of the extended filter. The FLAC requires smaller number of states therefore it need less computational effort. Fuzzy variable or the membership functions identify the grade of each input variables. Fuzzy adaptive filter
This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3D environment. The noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. Kalman fuzzy adaptive filter
Its relative simplicity and the straightforward implementation of the fuzzy operators, the fuzzy filter is able to compete with state-of-the-art filter techniques for noise reduction. Fuzzy nonlinear filters represent a well-established technology for multi channel image processing. The fuzzy filter scheme is sufficiently simple to enable fast hardware implementations. Conclusion