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Previews: From the Publisher Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. The Third Edition of this highly successful book has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. --This text refers to the Hardcover edition. Read more From the Back Cover CONTENTS Preface Acknowledgments Background and Preview Chapter 1 Stochastic Processes and Models Chapter 2 Wiener Filters Chapter 3 Linear Prediction Chapter 4 Method of Steepest Descent Chapter 5 Least-Mean-Square Adaptive Filters Chapter 6 Normalized Least-Mean-Square Adaptive Filters Chapter 7 Frequency-Domain and Subband Adaptive Filters Chapter 8 Method of Least Squares Chapter 9 Recursive Least-Square Adaptive Filters Chapter 10 Kalman Filters Chapter 11 Square-Root Adaptive Filters Chapter 12 Order-Recursive Adaptive Filters Chapter 13 Finite-Precision Effects Chapter 14 Tracking of Time-Varying Systems Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures Chapter 16 Blind Deconvolution Chapter 17 Back-Propagation Learning Epilogue Appendix A Complex Variables Appendix B Differentiation with Respect to a Vector Appendix C Method of Lagrange Multipliers Appendix D Estimation Theory Appendix E Eigenanalysis Appendix F Rotations and Reflections Appendix G Complex Wishart Distribution Glossary Bibliography Index Read more See all Editorial Reviews

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