Ai Machine Learning

Download Complex-Valued Neural Networks: Advances and Applications by Akira Hirose PDF

By Akira Hirose

Presents the most recent advances in complex-valued neural networks by means of demonstrating the idea in a variety of applications

Complex-valued neural networks is a quickly constructing neural community framework that makes use of advanced mathematics, displaying particular features in its studying, self-organizing, and processing dynamics. they're hugely compatible for processing advanced amplitude, composed of amplitude and section, that's one of many center thoughts in actual structures to house electromagnetic, gentle, sonic/ultrasonic waves in addition to quantum waves, particularly, electron and superconducting waves. This truth is a serious virtue in functional functions in diversified fields of engineering, the place signs are many times analyzed and processed in time/space, frequency, and section domains.

Complex-Valued Neural Networks: Advances and Applications covers state-of-the-art issues and purposes surrounding this well timed topic. Demonstrating complicated theories with a variety of purposes, together with conversation platforms, picture processing platforms, and brain-computer interfaces, this article deals complete assurance of:

  • Conventional complex-valued neural networks
  • Quaternionic neural networks
  • Clifford-algebraic neural networks

Presented by means of foreign specialists within the box, Complex-Valued Neural Networks: Advances and Applications is perfect for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians attracted to computational intelligence, man made intelligence, computing device studying theories, and algorithms.

Chapter 1 program Fields and basic benefits of Complex?Valued Neural Networks (pages 1–31): Akira Hirose
Chapter 2 Neural method studying on Complex?Valued Manifolds (pages 33–57): Simone Fiori
Chapter three N?Dimensional Vector Neuron and Its software to the N?Bit Parity challenge (pages 59–74): Tohru Nitta
Chapter four studying Algorithms in Complex?Valued Neural Networks utilizing Wirtinger Calculus (pages 75–102): Md. Faijul Amin and Kazuyuki Murase
Chapter five Quaternionic Neural Networks for Associative stories (pages 103–131): Teijiro Isokawa, Haruhiko Nishimura and Nobuyuki Matsui
Chapter 6 types of Recurrent Clifford Neural Networks and Their Dynamics (pages 133–151): Yasuaki Kuroe
Chapter 7 Meta?Cognitive Complex?Valued leisure community and Its Sequential studying set of rules (pages 153–183): Ramasamy Savitha, Sundaram Suresh and Narasimhan Sundarara
Chapter eight Multilayer Feedforward Neural community with Multi?Valued Neurons for Brain–Computer Interfacing (pages 185–208): Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin and Marc M. Van Hulle
Chapter nine Complex?Valued B?Spline Neural Networks for Modeling and Inverse of Wiener structures (pages 209–234): Xia Hong, Sheng Chen and Chris J. Harris
Chapter 10 Quaternionic Fuzzy Neural community for View?Invariant colour Face picture acceptance (pages 235–278): Wai equipment Wong, Gin Chong Lee, Chu Kiong bathroom lavatory, method Soong Lim and Raymond Lock

Show description

Read Online or Download Complex-Valued Neural Networks: Advances and Applications PDF

Best ai & machine learning books

How did we find out about the beginning of life?

Describes scientists' makes an attempt to determine how existence all started, together with such themes as spontaneous iteration and evolution.

Statistical Machine Translation

This introductory textual content to statistical laptop translation (SMT) presents the entire theories and techniques had to construct a statistical desktop translator, equivalent to Google Language instruments and Babelfish. ordinarily, statistical concepts enable automated translation structures to be equipped quick for any language-pair utilizing simply translated texts and customary software program.

Biomedical Natural Language Processing

Biomedical typical Language Processing is a accomplished travel throughout the vintage and present paintings within the box. It discusses all topics from either a rule-based and a computer studying procedure, and likewise describes every one topic from the viewpoint of either organic technological know-how and scientific medication. The meant viewers is readers who have already got a heritage in ordinary language processing, yet a transparent creation makes it obtainable to readers from the fields of bioinformatics and computational biology, to boot.

Additional info for Complex-Valued Neural Networks: Advances and Applications

Sample text

Special session on Clifford algebraic neural networks. In International Conference on Neural Information Processing (ICONIP) 2011 Shanghai. Springer, 2011. 55. Dong-Chul Park and Tae-Kyun Jung Jeong. Complex-bilinear recurrent neural networks for equilization of a digital satellite channel. IEEE Transactions on Neural Networks, 13(3):711-725, March 2002. 56. Bernard Picinbono and Pascal Chevalier. Widely linear estimation with complex data. IEEE Transactions on Signal Processing, 43(8):2030-2033, August 1995.

Geodesic stepping is based on the calculation of geodesic arcs in closed form and provides a geometrically sound way of moving from a point along a given direction on a Riemannian manifold proportionally to a learning stepsize. A method to compute a numerically optimal learning stepsize schedule is discussed as well, which resembles the line-search method on Euclidean spaces, termed geodesic-search method. An advantage of the devised geodesic-search method is that, in the space §£ (C), the geodesic curve is periodic of finite period, hence the learning stepsize belongs to a closed interval.

A matrix has a logarithm if and only if it is invertible. The logarithm is not unique, but if a matrix has no negative real eigenvalues, then it has a unique logarithm known as the principal logarithm, which is denoted by log(^). Furthermore, if, for any given matrix norm || · || it holds \\g — Ip11 < 1, where Ip denotes the identity element in Gl(p), then it holds that k=l In general, given matrices gi,g2 G G7(p, C), it holds that log(pi^2) Φ log(#i) + log(<72)· In practice, matrix exponential and logarithm may be computed efficiently by making use of the spectral decomposition of matrices or by various approximate methods [9, 31].

Download PDF sample

Rated 4.38 of 5 – based on 50 votes