1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. © Algorithms And Structures Of Adaptive Filtering: A Review Zerguine, A; Bettayeb, M KING FAHD UNIV PETROLEUM MINERALS, ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING; pp: 265-288; Vol: 23 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary Adaptive filtering techniques have been successfully implemented in various important applications such as echo cancellation, equalization, noise cancellation, and others. The rapid growth of computer technology made the on line implementation of adaptive filters possible and raised the interest of researchers in the development of new adaptive filtering algorithms for more challenging situations such as strong fading and large signal and noise distortions. In this paper, the authors present the state of the art of adaptive filtering algorithms. Both LMS and RLS families of algorithms are detailed. However, emphasis is also given to recently proposed least mean fourth (LMF) and mixed norm criteria. based adaptive filtering algorithms. The strengths and drawbacks of the above algorithms are also discussed. Simulation results are given for some popular algorithms. References: BARNES CW, 1985, IEEE T ACOUST SPEECH, V33, P595 BARTO AG, 1983, IEEE T SYST MAN CYB, V13, P834 BETTAYEB M, 1995, COMPUT ELECTR ENG, V21, P47 CARAYANNIS G, 1982, IEEE T ACOUST SPEECH, V30, P227 CARAYANNIS G, 1983, IEEE T ACOUST SPEECH, V31, P1394 CHAMBERS JA, 1994, ELECTRON LETT, V30, P1574 CHANG CH, 1995, SIGNAL PROCESS, V47, P145 CHEN JM, 1994, SIGNAL PROCESS, V37, P345 CIOFFI JM, 1984, IEEE T ACOUST SPEECH, V32, P304 CIOFFI JM, 1985, IEEE T ACOUST SPEECH, V33, P607 CIOFFI JM, 1986, IEEE T ACOUST SPEECH, V34, P77 CIOFFI JM, 1987, IEEE T CIRCUITS SYST, V34, P821 CLARK GA, 1981, IEEE T CIRCUITS SYST, V28, P584 COWAN CFN, 1979, ELECTRON LETT, V15, P35 Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

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3. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. © NAGUMO IJ, 1967, IEEE T AUTOMATIC CON, V28, P282 NIKIAS CL, 1993, HIGH ORDER SPECTRA A NISSEN CW, 1967, P IEEE, V55, P698 OPPENHEIM AV, 1975, DIGITAL SIGNAL PROCE PAZAITIS DI, 1995, ELECTRON LETT, V31, P1423 PEI SC, 1994, IEEE J SEL AREA COMM, V12, P1540 PROAKIS JG, 1983, DIGITAL COMMUNICATIO PROAKIS JG, 1992, DIGITAL SIGNAL PROCE QURESHI SUH, 1982, IEEE COMMUN MAGAZINE, V21, P9 QURESHI SUH, 1985, P IEEE, V73, P1349 ROBERTS RA, 1987, DIGITAL SIGNAL PROCE RUCK D, 1990, IEEE T NEURAL NETWOR, V1, P296 SAMSON C, 1982, INT J CONTROL, V31, P909 SATORIUS EH, 1979, IEEE T COMMUN, V27, P899 SAYED AH, 1994, IEEE SIGNAL PROC MAG, V11, P18 SCHROEDER J, 1991, SIGNAL PROCESS, V24, P193 SHAH SAH, 1995, IEE P-VIS IMAGE SIGN, V142, P187 SHAN M, 1993, P IEEE, V81, P986 SHYNK JJ, 1989, IEEE ASSP MAGAZINE, V28, P4 SHYNK JJ, 1990, IEEE T NEURAL NETWOR, V1, P268 SIU S, 1993, IEE PROC-F, V140, P43 SONDHI MM, 1967, BELL SYST TECH J, V46, P497 SONDHI MM, 1980, P IEEE, V68, P948 SORENSON HW, 1970, IEEE SPECTRUM, V7, P63 TANRIKULU O, 1994, ELECTRON LETT, V30, P189 TANRIKULU O, 1996, IEE P-VIS IMAGE SIGN, V143, P137 UNGERBOECK G, 1972, IBM J RES DEV, V16, P546 UNGERBOECK G, 1976, IEEE TRANS COMMUN, V24, P856 WALACH E, 1984, IEEE T INFORM THEORY, V30, P275 WATTERSON JW, 1990, IEEE T NEURAL NETWOR, V1, P298 WIDROW B, 1967, P IEEE, V55, P2143 WIDROW B, 1971, ASPECTS NETWORK SYST WIDROW B, 1975, P IEEE, V63, P1692 WIDROW B, 1976, PROC IEEE, V64, P1151 WIDROW B, 1985, ADAPTIVE SIGNAL PROC WIDROW B, 1990, P IEEE, V78, P1415 WIENER N, 1962, EXTRAPOLATION INTERP YARLAGADDA R, 1985, IEEE T ACOUST SPEECH, V33, P174 ZERGUINE A, UNPUB IEE P VIS IMAG ZERGUINE A, 1996, 13 ANN AS C SIGN SYS ZERGUINE A, 1996, ELECTRON LETT, V32, P1776 ZERGUINE A, 1997, IEEE T SIGNAL PROCES, V45, P1340 ZERGUINE A, 1997, P ICASSP MUNICH GERM, P2313 For pre-prints please write to: maamarab@kfupm.edu.sa Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

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