Ebook: Principles of Adaptive Filters and Self-learning Systems

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.

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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.

---

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.

Content:
Front Matter....Pages i-xxii
Adaptive Filtering....Pages 3-23
Linear Systems and Stochastic Processes....Pages 25-85
Optimisation and Least Squares Estimation....Pages 89-117
Parametric Signal and System Modelling....Pages 119-155
Optimum Wiener Filter....Pages 159-172
Optimum Kalman Filter....Pages 173-195
Power Spectral Density Analysis....Pages 197-224
Adaptive Finite Impulse Response Filters....Pages 227-245
Frequency Domain Adaptive Filters....Pages 247-256
Adaptive Volterra Filters....Pages 257-265
Adaptive Control Systems....Pages 267-285
Introduction to Neural Networks....Pages 289-311
Introduction to Fuzzy Logic Systems....Pages 313-324
Introduction to Genetic Algorithms....Pages 325-336
Applications of Adaptive Signal Processing....Pages 339-353
Generic Adaptive Filter Structures....Pages 355-371
Back Matter....Pages 373-386

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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.

Content:
Front Matter....Pages i-xxii
Adaptive Filtering....Pages 3-23
Linear Systems and Stochastic Processes....Pages 25-85
Optimisation and Least Squares Estimation....Pages 89-117
Parametric Signal and System Modelling....Pages 119-155
Optimum Wiener Filter....Pages 159-172
Optimum Kalman Filter....Pages 173-195
Power Spectral Density Analysis....Pages 197-224
Adaptive Finite Impulse Response Filters....Pages 227-245
Frequency Domain Adaptive Filters....Pages 247-256
Adaptive Volterra Filters....Pages 257-265
Adaptive Control Systems....Pages 267-285
Introduction to Neural Networks....Pages 289-311
Introduction to Fuzzy Logic Systems....Pages 313-324
Introduction to Genetic Algorithms....Pages 325-336
Applications of Adaptive Signal Processing....Pages 339-353
Generic Adaptive Filter Structures....Pages 355-371
Back Matter....Pages 373-386
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