Supervised Learning With Complex Valued Neural Networks
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Supervised Learning With Complex Valued Neural Networks
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Author : Sundaram Suresh
language : en
Publisher: Springer
Release Date : 2012-07-28
Supervised Learning With Complex Valued Neural Networks written by Sundaram Suresh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-28 with Technology & Engineering categories.
Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
Supervised Learning With Complex Valued Neural Networks
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Author : Sundaram Suresh
language : en
Publisher: Springer
Release Date : 2012-07-28
Supervised Learning With Complex Valued Neural Networks written by Sundaram Suresh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-28 with Computers categories.
Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
Complex Valued Neural Networks
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Author : Akira Hirose
language : en
Publisher: World Scientific
Release Date : 2003
Complex Valued Neural Networks written by Akira Hirose and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.
Complex Valued Neural Networks
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Author : Akira Hirose
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-08
Complex Valued Neural Networks written by Akira Hirose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-08 with Computers categories.
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
Complex Valued Neural Networks
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Author : Akira Hirose
language : en
Publisher: Springer
Release Date : 2007-01-11
Complex Valued Neural Networks written by Akira Hirose and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-11 with Computers categories.
This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more.
Complex Valued Neural Networks With Multi Valued Neurons
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Author : Igor Aizenberg
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-24
Complex Valued Neural Networks With Multi Valued Neurons written by Igor Aizenberg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-24 with Computers categories.
Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.
Complex Valued Neural Networks Utilizing High Dimensional Parameters
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Author : Nitta, Tohru
language : en
Publisher: IGI Global
Release Date : 2009-02-28
Complex Valued Neural Networks Utilizing High Dimensional Parameters written by Nitta, Tohru and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-28 with Computers categories.
"This book covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters"--Provided by publisher.
Complex Valued Neural Networks
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Author : Akira Hirose
language : en
Publisher:
Release Date : 2003-01-01
Complex Valued Neural Networks written by Akira Hirose and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Neural networks categories.
Elements Of Artificial Neural Networks
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Author : Kishan Mehrotra
language : en
Publisher: MIT Press
Release Date : 1997
Elements Of Artificial Neural Networks written by Kishan Mehrotra and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.
Artificial Neural Networks The Brain Behind Ai
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Author : Jayesh Ahire
language : en
Publisher: Lulu.com
Release Date :
Artificial Neural Networks The Brain Behind Ai written by Jayesh Ahire and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.