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Applied Artificial Higher Order Neural Networks for Control and Recognition

Applied Artificial Higher Order Neural Networks for Control and Recognition

Ming Zhang
Copyright: © 2016 |Pages: 511
ISBN13: 9781522500636|ISBN10: 1522500634|EISBN13: 9781522500643
DOI: 10.4018/978-1-5225-0063-6
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MLA

Zhang, Ming, editor. Applied Artificial Higher Order Neural Networks for Control and Recognition. IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0063-6

APA

Zhang, M. (Ed.). (2016). Applied Artificial Higher Order Neural Networks for Control and Recognition. IGI Global. https://doi.org/10.4018/978-1-5225-0063-6

Chicago

Zhang, Ming, ed. Applied Artificial Higher Order Neural Networks for Control and Recognition. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0063-6

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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.

Table of Contents

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Front Materials
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Title Page
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Copyright Page
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Advances in Computational Intelligence and Robotics (ACIR) Book Series
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Dedication
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Editorial Advisory Board
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Preface
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Acknowledgment
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Chapters
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Artificial Higher Order Neural Networks for Control
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Artificial Higher Order Neural Networks for Recognition
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Artificial Higher Order Neural Networks for Simulation and Predication
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Artificial Higher Order Neural Network Models and Applications
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Back Materials
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Compilation of References
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About the Contributors
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Index
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