 BROWSE
 USED
 STAFF PICKS
 GIFTS + GIFT CARDS
 SELL BOOKS
 BLOG
 EVENTS
 FIND A STORE
 800.878.7323

On Order
$115.50
New Hardcover
Currently out of stock.
available for shipping or prepaid pickup only
Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:
Adaptive and Learning Systems for Signal Processing, Communications and Control #38: Neural Based Orthogonal Data Fitting: The Exin Neural Networksby Giansalvo Cirrincione
Synopses & ReviewsPublisher Comments:The presentation of a novel theory in orthogonal regression
The literature about neuralbased algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, NeuralBased Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem. The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as:
In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing realtime problems and show accurate solutions. NeuralBased Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers. Book News Annotation:As an alternative to the nearly ubiquitous ordinary least squares techniques for modeling systems using linear regression models, the Cirrinciones Giansalve (U. de PicardieJules Verne, Amien) and Maurizio (control and signal processing, U. de Technologie de BelfortMontbéliard, Belfort) explain some iterative total least squares methods that are defined in the literature as neural. Focusing on EXIN neurons, which they created themselves in recent years, they introduce, explain, and analyze them and compare them to the other neural approaches. Their treatment is mainly theoretical, dealing with mathematical and numerical aspects, and even the simulations are selected for illustrating the theory rather than for any practical purpose. Their many applications will appear in the next book. Annotation Â©2011 Book News, Inc., Portland, OR (booknews.com)
Synopsis:Written by two leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Where most books on the subject are dedicated to PCA (principal component analysis) and consider MCA (minor component analysis) as simply a consequence, this is the fist book to start from the MCA problem and arrive at important conclusions about the PCA problem.
Synopsis:The presentation of a novel theory in orthogonal regression
The literature about neuralbased algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, NeuralBased Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem. The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as:
In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing realtime problems and show accurate solutions. NeuralBased Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers. About the AuthorGIANSALVO CIRRINCIONE, PHD, is an assistant professor at the University of PicardieJules Verne, Amiens, France. His current research interests are neural networks, data analysis, computer vision, intelligent control, applied mathematics, brain models, and system identification. Email address: exin@upicardie.fr
MAURIZIO CIRRINCIONE, PHD, is a full professor of control and signal processing at the University of Technology of BelfortMontbéliard, France. His current research interests are neural networks, modeling and control, system identification, data analysis, intelligent control, and electrical machines and drives. Email address: maurizio.cirrincione@utbm.fr Table of ContentsForeword.
Preface. 1 The Total Least Squares Problems. 1.1 Introduction. 1.2 Some TLS Applications. 1.3 Preliminaries. 1.4 Ordinary Least Squares Problems. 1.5 Basic TLS Problem. 1.6 Multidimensional TLS Problem. 1.7 Nongeneric Unidimensional TLS Problem. 1.8 Mixed OLS–TLS Problem. 1.9 Algebraic Comparisons Between TLS and OLS. 1.10 Statistical Properties and Validity. 1.11 Basic Data Least Squares Problem. 1.12 The Partial TLS Algorithm. 1.13 Iterative Computation Methods. 1.14 Rayleigh Quotient Minimization Non Neural and Neural Methods. 2 The MCA EXIN Neuron. 2.1 The Rayleigh Quotient. 2.2 The Minor Component Analysis. 2.3 The MCA EXIN Linear Neuron. 2.4 The Rayleigh Quotient Gradient Flows. 2.5 The MCA EXIN ODE Stability Analysis. 2.6 Dynamics of the MCA Neurons. 2.7 Fluctuations (Dynamic Stability) and Learning Rate. 2.8 Numerical Considerations. 2.9 TLS Hyperplane Fitting. 2.10 Simulations for the MCA EXIN Neuron. 2.11 Conclusions. 3 Variants of the MCA EXIN Neuron. 3.1 HighOrder MCA Neurons. 3.2 The Robust MCA EXIN Nonlinear Neuron (NMCA EXIN). 3.3 Extensions of the Neural MCA. 4 Introduction to the TLS EXIN Neuron. 4.1 From MCA EXIN to TLS EXIN. 4.2 Deterministic Proof and Batch Mode. 4.3 Acceleration Techniques. 4.4 Comparison with TLS GAO. 4.5 A TLS Application: Adaptive IIR Filtering. 4.6 Numerical Considerations. 4.7 The TLS Cost Landscape: Geometric Approach. 4.8 First Considerations on the TLS Stability Analysis. 5 Generalization of Linear Regression Problems. 5.1 Introduction. 5.2 The Generalized Total Least Squares (GeTLS EXIN) Approach. 5.3 The GeTLS Stability Analysis. 5.4 Neural Nongeneric Unidimensional TLS. 5.5 Scheduling. 5.6 The Accelerated MCA EXIN Neuron (MCA EXIN+). 5.7 Further Considerations. 5.8 Simulations for the GeTLS EXIN Neuron. 6 The GeMCA EXIN Theory. 6.1 The GeMCA Approach. 6.2 Analysis of Matrix K. 6.3 Analysis of the Derivative of the Eigensystem of GeTLS EXIN. 6.4 Rank One Analysis Around the TLS Solution. 6.5 The GeMCA Spectra. 6.6 Qualitative Analysis of the Critical Points of the GeMCA EXIN Error Function. 6.7 Conclusion. References. Index. What Our Readers Are SayingBe the first to add a comment for a chance to win!Product Details
Related Subjects
» Arts and Entertainment » Architecture » Architects


