For courses in Adaptive Filters. Simon Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
- Consolidates the mathematical treatment of linear adaptive filters.
- Improves the presentation of material on statistical LMS theory and statistical RLS theory.
- Expands the treatment of normalized LMS filters, and introduces the more general case of affine projection filters.
- Introduces sub-band adaptive filters.
- Repositions the teaching of Kalman filters after the treatment of RLS filters, thereby enhancing the unified treatment of square-root adaptive filters and order recursive adaptive filters.