Principles of Adaptive Filters and Self-learning Systems

• Κύριος συγγραφέας: Zaknich, Anthony (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Grimble, Michael J. (Επιμελητής έκδοσης), Johnson, Michael A. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: London : Springer London, 2005.
• Σειρά: Advanced Textbooks in Control and Signal Processing,
• Θέματα: Engineering.; Artificial intelligence.; Mechanical engineering.; Control engineering.; Robotics.; Mechatronics.; Electrical engineering.; Signal, Image and Speech Processing.; Artificial Intelligence (incl. Robotics).; Control, Robotics, Mechatronics.; Electrical Engineering.; Mechanical Engineering.; Engineering, general.

 Kalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book How can a signal be processed for which there are few or no a priori data? Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing applications. Important topics are introduced and discussed sufficiently to give the reader adequate background for confident further investigation. The material is presented in a progression from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications. Features: • Comprehensive review of linear and stochastic theory. • Design guide for practical application of the least squares estimation method and Kalman filters. • Study of classical adaptive systems together with neural networks, genetic algorithms and fuzzy logic systems and their combination to deal with such complex problems as underwater acoustic signal processing. • Tutorial problems and exercises which identify the significant points and demonstrate the practical relevance of the theory. • PDF Solutions Manual, available to tutors from springeronline.com, containing not just answers to the tutorial problems but also course outlines, sample examination material and project assignments to help in developing a teaching programme and to give ideas for practical investigations. 

 Adaptive Filtering -- Linear Systems and Stochastic Processes -- Modelling -- Optimisation and Least Squares Estimation -- Parametric Signal and System Modelling -- Classical Filters and Spectral Analysis -- Optimum Wiener Filter -- Optimum Kalman Filter -- Power Spectral Density Analysis -- Adaptive Filter Theory -- Adaptive Finite Impulse Response Filters -- Frequency Domain Adaptive Filters -- Adaptive Volterra Filters -- Adaptive Control Systems -- Nonclassical Adaptive Systems -- to Neural Networks -- to Fuzzy Logic Systems -- to Genetic Algorithms -- Adaptive Filter Application -- Applications of Adaptive Signal Processing -- Generic Adaptive Filter Structures.