Free Kalman Filtering And Neural Networks Ebooks Online

                
              
Free 
Kalman Filtering And Neural Networks Ebooks Online
State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:  An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter  Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.
Hardcover: 304 pages
Publisher: Wiley-Interscience; 1 edition (October 8, 2001)
Language: English
ISBN-10: 0471369985
ISBN-13: 978-0471369981
Product Dimensions:  6.4 x 0.8 x 9.7 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:  5.0 out of 5 stars  See all reviews (1 customer review)
Best Sellers Rank: #1,654,418 in Books (See Top 100 in Books)   #175 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Neural Networks   #2643 in Books > Computers & Technology > Certification   #4969 in Books > Engineering & Transportation > Engineering > Telecommunications & Sensors
This book is very coherent in its exposition of ideas and reads almost like an "authored" book. There are some redundancy in explanation of ideas by different authors, but proper references are made to other chapters in the book (that were written by other authors) for a complete explanation.You can find a self contained explanation of Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter as applied to machine learning, where you have some parameter values to be automatically identified such as in weights for neural networks.My interest was primarily in Unscented Kalman Filter and the book was detailed enough so that I could code my own Unscented Kalman Filter and reproduce some examples in the book. In the process, I had to look up on the internet on Robbins-Monro Algorithm because the book lacked a detailed explanation about it even though it was a suggested method for updating innovation covariance. Overall, the explanations were clear, and it has been a smooth process from reading this book to applying the algorithms to my own problem at hand.
Kalman Filtering and Neural Networks Deep Learning: Natural Language Processing in Python with Recursive Neural Networks: Recursive Neural (Tensor) Networks in Theano (Deep Learning and Natural Language Processing Book 3) Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press) Principles of Neural Science, Fifth Edition (Principles of Neural Science (Kandel)) Candlepower: Advanced Candlestick Pattern Recognition and Filtering Techniques for Trading Stocks and Futures Helpers in My Community (Bobbie Kalman's Leveled Readers: My World: G) Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano (Machine Learning in Python) Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python) Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks Deep Learning Neural Networks: Design and Case Studies Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications (International Series on Computational Intelligence) Deep Learning for Business with R: A Very Gentle Introduction to Business Analytics Using Deep Neural Networks Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science Introduction to the Math of Neural Networks An Introduction to Neural Networks Neural Networks: A Comprehensive Foundation (2nd Edition) Elements of Artificial Neural Networks (Complex Adaptive Systems) Manual of Microsurgery on the Laboratory Rat. Part 1: General Information and Experimental Techniques (Techniques in the Behavioral and Neural Science, 4) (Pt.1) 