Applied Artificial Higher Order Neural Networks for Control and Recognition

Applied Artificial Higher Order Neural Networks for Control and Recognition

Ming Zhang (Christopher Newport University, USA)
Release Date: May, 2016|Copyright: © 2016 |Pages: 511
ISBN13: 9781522500636|ISBN10: 1522500634|EISBN13: 9781522500643|DOI: 10.4018/978-1-5225-0063-6

Description

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless.

Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Ant colony optimization
  • Artificial neural networks
  • Control Signals
  • Data Prediction
  • System Control
  • System Monitoring

Reviews and Testimonials

This is the first book that introduces higher order neural networks (HONNs) to researchers and others working in the fields of control and recognition. HONNs are an open box neural networks tool comparable to traditional artificial neural networks. This book explains how HONNs can approximate any nonlinear data to any degree of accuracy, allowing researchers to understand why HONNs are much easier to use, and why they can have better nonlinear data recognition accuracy than SAS nonlinear (NLIN) procedures. It introduces HONN group models and adaptive HONNs, which can simulate not only nonlinear data, but also discontinuous and unsmooth nonlinear data. Eighteen chapters are divided into four sections: artificial higher order neural networks for control; artificial higher order neural networks for recognition; artificial higher order neural networks for simulation and prediction; artificial higher order neural network models and applications.

– ProtoView Reviews

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Ming Zhang received a M.S. degree in information processing and a Ph.D. degree in the research area of computer vision from the East China Normal University, Shanghai, China, in 1982 and 1989, respectively. He held Postdoctoral Fellowships in artificial neural networks with the Shanghai Institute of Technical Physics, Chinese Academy of the Sciences in 1989 and the National Oceanic and Atmospheric Administration, USA National Research Council in 1991. He was a face recognition airport security system project manager and was a Ph.D. co-supervisor at the University of Wollongong, Australia in 1992. Since 1994, he has been a lecturer at the Monash University, Australia. From 1995 to 1999, he was a lecturer and a senior lecturer and a Ph.D. supervisor at the University of Western Sydney, Australia. He also held a Senior Research Associate Fellowship in artificial neural networks with the National Oceanic and Atmospheric Administration, USA National Research Council in 1999. Since 2000, he has been an associate professor at Christopher Newport University, VA, USA. He is currently a full Professor and a graduate student supervisor in computer science at the Christopher Newport University, VA, USA.