Fuzzy adaptive filters, with application to nonlinear channel equalization
IEEE Transactions on Fuzzy SystemsPublished 1 January 1993
L.-X. Wang, Jerry M. Mendel
Citations161
SJR quartileQ1
SJR score3.61
SNIP2.80
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithms, which are applied to nonlinear communication channel equalization problems.
Abstract
Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come from human experts or are determined during the adaptation procedure by matching input-output data pairs; (3) construct a filter based on the set of rules; and (4) update the free parameters of the filter using the RLS algorithm. The design procedure for the LMS fuzzy adaptive filter is similar. The most important advantage of the fuzzy adaptive filters is that linguistic information (in the form of fuzzy IF-THEN rules) and numerical information (in the form of input-output pairs) can be combined in the filters in a uniform fashion. The filters are applied to nonlinear communication channel equalization problems.>
Keywords
Computer ScienceEngineering
IEEE Transactions on Systems Man and CyberneticsOutline of a New Approach to the Analysis of Complex Systems and Decision Processes
8,667 Citations1973Lotfi A. Zadeh
By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
Mathematics in Science and Engineering/Mathematics in science and engineeringFuzzy Sets and Systems - Theory and Applications
6,430 Citations1980Didier Dubois, Henri Prade
IEEE Transactions on Systems Man and CyberneticsFuzzy logic in control systems: fuzzy logic controller. I
5,581 Citations1990C.C. Lee
This paper presents a survey of the FLC; describes a general methodology for constructing an FLC and assessing its per formance; and points to problems which need further research.
Stable Adaptive Systems
3,982 Citations1989Kumpati S. Narendra, Anuradha M. Annaswamy
Stability theory simple adaptive systems adaptive observers the control problem persistent excitation error models robust adaptive controlThe control problem - relaxation of assumptions multivariable adaptive systems applications of adaptive control.
European Journal of Operational ResearchFuzzy sets, uncertainty, and information
3,193 Citations1989
The fuzzy sets uncertainty and information is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
IEEE Transactions on Systems Man and CyberneticsGenerating fuzzy rules by learning from examples
2,876 Citations1992L.-X. Wang, Jerry M. Mendel
IEEE Transactions on Neural NetworksFuzzy basis functions, universal approximation, and orthogonal least-squares learning
2,771 Citations1992L.-X. Wang, Jerry M. Mendel
Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy.
IEEE Transactions on Systems Man and CyberneticsFuzzy logic in control systems : fuzzy logic controller. Part II
2,227 Citations1990Chuen Chien Lee
The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy.
IEEE Transactions on Fuzzy SystemsStable adaptive fuzzy control of nonlinear systems
1,599 Citations1993L.-X. Wang
IEEE Transactions on Systems Man and CyberneticsFuzzy logic in control systems: fuzzy logic controller. II
1,304 Citations1990C.C. Lee
The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined and several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated.
Nonlinear Digital Filters
1,111 Citations1990Ioannis Pitas, A.N. Venetsanopoulos
This chapter discusses digital filters based on order statistics, Morphological image and signal processing, and Adaptive nonlinear filters.
Fuzzy systems are universal approximators
1,064 Citations2003L.-X. Wang
The Stone-Weierstrass theorem is used to prove that fuzzy systems with product inference, centroid defuzzification, and a Gaussian membership function are capable of approximating any real continuous function on a compact set to arbitrary accuracy.
IEEE Signal Processing MagazineAdaptive polynomial filters
544 Citations1991V.J. Mathews
The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion or a recursive nonlinear difference equation.
Back-propagation fuzzy system as nonlinear dynamic system identifiers
451 Citations2003L.-X. Wang, Jerry M. Mendel
The authors develop a training algorithm, similar to the backpropagation algorithm for neural networks, to train fuzzy systems to match desired input-output pairs and demonstrate how the fuzzy system learns to match an unknown nonlinear mapping as training progresses and that performance is improved by incorporating linguistic rules.
Signal ProcessingAdaptive equalization of finite non-linear channels using multilayer perceptrons
281 Citations1990Sheng Chen, Gavin J. Gibson +2 more
It is shown that difficulties associated with channel non-linearities and additive noise correlation can be overcome by the use of equalizers employing a multi-layer perceptron structure, providing further evidence that the neural network approach proposed recently by Gibson et al. is a general solution to the problem of equalization in digital communications systems.
IEEE Journal on Selected Areas in CommunicationsAdaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission
191 Citations1984E. Biglieri, A. Gersho +2 more
Two related cancellation techniques based on "tentative" decisions are proposed for removing intersymbol interference, due to channel nonlinearities, for voiceband data transmission, and can offer substantial performance improvements for 16-point QAM systems.
Bell System Technical JournalAdaptive Equalization of Channel Nonlinearities in QAM Data Transmission Systems
145 Citations1978D.D. Falconer
An investigation of a QAM receiver incorporating adaptive equalization of nonlinearities as well as adaptive decision feedback equalization and data-aided carrier recovery for mitigation of linear distortion and phase jitter, respectively is reported on.
Signal ProcessingReconstruction of binary signals using an adaptive radial-basis-function equalizer
144 Citations1991Sheng Chen, Gavin J. Gibson +2 more
It is shown that the optimal equalization solution under the classical architecture of making decisions symbol-by-symbol is an inherently non-linear problem and therefore some degree of non- linear decision making ability is desirable in the equalizer structure.
Kluwer international series in engineering and computer scienceAdaptive Filters and Equalisers
78 Citations1988B. Mulgrew, C.F.N. Cowan
The Kalman Filter as an IIR Equaliser and Limitations and Further Work, Appendix A The Fast Kalman Algorithm, The RLS Lattice Algorithm and Appendix C Circular and Linear Convolution.
IEEE Transactions on ComputersPotential Functions in Mathematical Pattern Recognition
39 Citations1969William S. Meisel
A class of methods for pattern classification using a set of samples of a type directly derived from concepts related to superposition, and it is shown that smooth potential functions exist that will separate arbitrary sets of sample points.