Complex-Valued Neural Networks

Advances and Applications

By John Wiley & Sons | Publisher: Wiley

About the book

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.

Editions of Complex-Valued Neural Networks

Hardcover
ISBN 9781118344606
EBook
ISBN 9781118590140
EBook
ISBN 9781118590072
EBook
ISBN 9781118590065

Read an Excerpt

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.

Frequently Asked Questions

What is Complex-Valued Neural Networks about?

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.

What core themes, tropes, or subjects are explored in Complex-Valued Neural Networks?

Computing and Information Technology > Computer science > Artificial intelligence (AI) > Neural networks and fuzzy systems

Where can I read a sample of Complex-Valued Neural Networks?

You can read an official preview of the few pages here https://www.book2look.com/book/9781118344606

Is Complex-Valued Neural Networks part of a series, and can it be read as a standalone?

Yes it is a part of series IEEE Press Series on Computational Intelligence

Who is/are the Author/s of the book Complex-Valued Neural Networks?

John Wiley & Sons

Who is the Publisher of the book Complex-Valued Neural Networks?

Wiley

What are the ISBN numbers for the physical and digital editions?

Complex-Valued Neural Networks is available as hardcover(ISBN 9781118344606) and ebook(ISBN 9781118590140) and ebook(ISBN 9781118590072) and ebook(ISBN 9781118590065)

Where can I buy Complex-Valued Neural Networks online or support local independent bookshops?

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