Last edited by Dairisar
Tuesday, July 14, 2020 | History

1 edition of Complex-Valued Neural Networks found in the catalog.

Complex-Valued Neural Networks

by Akira Hirose

  • 343 Want to read
  • 14 Currently reading

Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

    Subjects:
  • Engineering,
  • Computational intelligence,
  • Artificial intelligence,
  • Artificial Intelligence (incl. Robotics)

  • Edition Notes

    Statementby Akira Hirose
    SeriesStudies in Computational Intelligence -- 400
    ContributionsSpringerLink (Online service)
    The Physical Object
    Format[electronic resource] /
    ID Numbers
    Open LibraryOL27025539M
    ISBN 109783642276323

    Complex-valued neural networks fertilize electronics --Neural networks: the characteristic viewpoints --Complex-valued neural networks: distinctive features --Constructions and dynamics of neural networks --Land-surface classification with unevenness and reflectance taken into consideration --Adaptive radar system to visualize antipersonnel. In this chapter, we introduce the multi-valued neuron. First of all, in Section we consider the essentials of the theory of multiple-valued logic over the field of complex : Igor Aizenberg.

    Meta-cognition in Complex-valued Neural Networks Problem Definition Meta-cognitive Fully Complex-valued Radial Basis Function Network Complex-Valued Self-Regulatory Resource Allocation Network Issues in Mc-FCRBF and CSRAN Meta-cognitive Fully Complex-valued Relaxation Network Any classical monograph/text book on neural networks claims that to learn the XOR function a network from at least three neurons is needed. This is true for the real-valued neurons and real-valued neural networks. However, this is not true for the complex-valued neurons!!! A jump to the complex domain is a right way toFile Size: 8MB.

    Complex-Valued Neural Networks | Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applicationsComplex-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. 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.


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Complex-Valued Neural Networks by Akira Hirose Download PDF EPUB FB2

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.

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields.

In the second edition the recent trends in. 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 : Hardcover.

Application Fields and Fundamental Merits of Complex-Valued Neural Networks Neural System Learning on Complex-Valued Manifolds -Dimensional Vector Neuron and Its Application to the. This book is the first monograph ever on complex-valued neural networks, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant : Springer-Verlag Berlin Heidelberg.

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant by:   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. This book is the first monograph ever on complex-valued neural networks, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. Complex-valued neural networks (CVNNs) deal with information in complex domain with complex-valued parameters and variables.

As explained in Section in relation to physicality, neural Author: Akira Hirose. This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant : Springer-Verlag Berlin Heidelberg.

Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely trating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of.

Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent years.

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics.

Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent.

Complex-Valued Neural Networks: /ch The usual real-valued artificial neural networks have been applied to various fields such as telecommunications, robotics, bioinformatics, image processingCited by: 3.

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. 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 : Wiley.

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields.

Comparison of the Complex Valued and Real Valued Neural Networks Trained with Gradient Descent and Random Search Algorithms Hans Georg Zimmermann1, Alexey Minin2,3 and Victoria Kusherbaeva3 1- Siemens AG - Corporate Technology Muenchen, Germany.

2- Technischen Universitat Muenchen - Robotics dept. Muenchen, Germany. This is the first book focusing on the Complex-Valued Neural Networks. In these years the complex-valued neural networks expand the application fields in optoelectronic imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems as well as artificial neural information processing.

Complex value networks allows the input/variables in networks being complex, while the loss keeping real. In this post, I will derive back propagation formula for complex valued neural network units.

provide a table of reference for widely used complex neural network units. At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust Cited by: springer, This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published inwhich lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields.

In the second edition the recent trends in .