Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

566 Pages·2021·13.547 MB·English

by  Ming Zhang

Emerging Capabilities and
Applications of Artificial
Higher Order Neural
Networks
Ming Zhang
Christopher Newport University, USA
A volume in the Advances in
Computational Intelligence and
Robotics (ACIR) Book Series
Published in the United States of America by
IGI Global
Engineering Science Reference (an imprint of IGI Global)
701 E. Chocolate Avenue
Hershey PA, USA 17033
Tel: [phone]
Fax: [phone]
E-mail: [email protected]
Web site: http://www.igi-global.com
Copyright © 2021 by IGI Global. All rights reserved. No part of this publication may be
reproduced, stored or distributed in any form or by any means, electronic or mechanical, including
photocopying, without written permission from the publisher.
Product or company names used in this set are for identification purposes only. Inclusion of the
names of the products or companies does not indicate a claim of ownership by IGI Global of the
trademark or registered trademark.
Library of Congress Cataloging-in-Publication Data
Names: Zhang, Ming, 1949 July 29- author.
Title: Emerging capabilities and applications of artificial higher order
neural networks / by Ming Zhang.
Description: Hershey, PA : Engineering Science Reference, an imprint of IGI
Global, [2020] | Includes bibliographical references and index. |
Summary: “This book explores the emerging capabilities and applications
of artificial higher order neural networks in the fields of economics,
business, modeling, simulation, control, recognition, computer science,
and engineering”-- Provided by publisher.
Identifiers: LCCN 2019054311 (print) | LCCN 2019054312 (ebook) | ISBN
9781799835639 (hardcover) | ISBN 9781799835646 (paperback) | ISBN
9781799835653 (ebook)
Subjects: LCSH: Neural networks (Computer science)--Industrial
applications. | Engineering--Data processing. | Business--Decision
making--Data processing.
Classification: LCC QA76.87 .Z4745 2020 (print) | LCC QA76.87 (ebook) |
DDC 006.3/2--dc23
LC record available at https://lccn.loc.gov/2019054311
LC ebook record available at https://lccn.loc.gov/2019054312

This book is published in the IGI Global book series Advances in Computational Intelligence and
Robotics (ACIR) (ISSN: 2327-0411; eISSN: 2327-042X)
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material.
The views expressed in this book are those of the authors, but not necessarily of the publisher.
For electronic access to this publication, please contact: [email protected].
Advances in Computational
Intelligence and Robotics
Ivan Giannoccaro
(ACIR) Book Series University of Salento, Italy
ISSN:2327-0411
EISSN:2327-042X
Editor-in-Chief: Ivan Giannoccaro, University of Salento, Italy
Mission
While intelligence is traditionally a term applied to humans and human cognition, technology
has progressed in such a way to allow for the development of intelligent systems able to
simulate many human traits. With this new era of simulated and artificial intelligence, much
research is needed in order to continue to advance the field and also to evaluate the ethical
and societal concerns of the existence of artificial life and machine learning.
The Advances in Computational Intelligence and Robotics (ACIR) Book Series
encourages scholarly discourse on all topics pertaining to evolutionary computing, artificial
life, computational intelligence, machine learning, and robotics. ACIR presents the latest
research being conducted on diverse topics in intelligence technologies with the goal of
advancing knowledge and applications in this rapidly evolving field.
Coverage
• Fuzzy Systems
IGI Global is currently accepting
• Automated Reasoning
manuscripts for publication within this
• Neural Networks series. To submit a proposal for a volume in
• Machine Learning this series, please contact our Acquisition
• Artificial Life Editors at [email protected] or
• Synthetic Emotions visit: http://www.igi-global.com/publish/.
• Cognitive Informatics
• Algorithmic Learning
• Natural Language Processing
• Brain Simulation
The Advances in Computational Intelligence and Robotics (ACIR) Book Series (ISSN 2327-0411) is published by
IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of
titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the
series. For pricing and ordering information please visit http://www.igi-global.com/book-series/advances-computational-
intelligence-robotics/73674. Postmaster: Send all address changes to above address. Copyright © 2021 IGI Global. All rights,
including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any
form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or information and
retrieval systems – without written permission from the publisher, except for non commercial, educational use, including
classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global.
Titles in this Series
For a list of additional titles in this series, please visit:
http://www.igi-global.com/book-series/advances-computational-intelligence-robotics/73674
Machine Learning Techniques for Pattern Recognition and Information Security
Mohit Dua (National Institute of Technology Kurukshetra, India) and Ankit Kumar Jain
(National Institute of Technology, Kurukshetra, India)
Engineering Science Reference • © 2021 • 300pp • H/C (ISBN: 9781799832997) • US
$225.00
Driving Innovation and Productivity Through Sustainable Automation
Ardavan Amini (EsseSystems, UK) Stephen Bushell (Bushell Investment Group, UK) and
Arshad Mahmood (Birmingham City University, UK)
Engineering Science Reference • © 2021 • 275pp • H/C (ISBN: 9781799858799) • US
$245.00
Examining Optoelectronics in Machine Vision and Applications in Industry 4.0
Oleg Sergiyenko (Autonomous University of Baja California, Mexico) Julio C. Rodriguez-
Quiñonez (Autonomous University of Baja California, Mexico) and Wendy Flores-Fuentes
(Autonomous University of Baja California, Mexico)
Engineering Science Reference • © 2021 • 346pp • H/C (ISBN: 9781799865223) • US
$215.00
Machine Learning Applications in Non-Conventional Machining Processes
Goutam Kumar Bose (Haldia Institute of Technology, India) and Pritam Pain (Haldia Institute
of Technology, India)
Engineering Science Reference • © 2021 • 313pp • H/C (ISBN: 9781799836247) • US
$195.00
Artificial Neural Network Applications in Business and Engineering
Quang Hung Do (University of Transport Technology, Vietnam)
Engineering Science Reference • © 2021 • 275pp • H/C (ISBN: 9781799832386) • US
$245.00
For an entire list of titles in this series, please visit:
http://www.igi-global.com/book-series/advances-computational-intelligence-robotics/73674
701 East Chocolate Avenue, Hershey, PA 17033, USA
Tel: [phone] x100 • Fax: [phone]
E-Mail: [email protected] • www.igi-global.com
This book is dedicated to my wife, Zhao Qing Zhang.
Table of Contents
Preface.................................................................................................................xiii
Acknowledgment...............................................................................................xxii
Section 1
Models of Artificial Higher Order Neural Networks
Chapter 1
Models.of.Artificial.Higher.Order.Neural.Networks..............................................1
Chapter 2
Models.of.Artificial.Multi-Polynomial.Higher.Order.Neural.Networks..............97
Chapter 3
Group.Models.of.Artificial.Polynomial.and.Trigonometric.Higher.Order.
Neural.Networks.................................................................................................137
Section 2
Artificial Higher Order Neural Networks for Economics and
Business
Chapter 4
SAS.Nonlinear.Models.or.Artificial.Higher.Order.Neural.Network.Nonlinear.
Models?...............................................................................................................174
Chapter 5
Time.Series.Data.Analysis.by.Ultra-High.Frequency.Trigonometric.Higher.
Order.Neural.Networks.......................................................................................218
Chapter 6
Financial.Data.Prediction.by.Artificial.Sine.and.Cosine.Trigonometric.
Higher.Order.Neural.Networks...........................................................................262

Section 3
Artificial Higher Order Neural Networks for Modeling and
Simulation
Chapter 7
Data.Classification.Using.Ultra-High.Frequency.SINC.and.Trigonometric.
Higher.Order.Neural.Networks...........................................................................303
Chapter 8
Data.Simulations.Using.Cosine.and.Sigmoid.Higher.Order.Neural.Networks..346
Chapter 9
Rainfall.Estimation.Using.Neuron-Adaptive.Higher.Order.Neural.Networks....375
Section 4
Artificial Higher Order Neural Networks for Control and
Recognition
Chapter 10
Control.Signal.Generator.Based.on.Ultra-High.Frequency.Polynomial.and.
Trigonometric.Higher.Order.Neural.Networks...................................................416
Chapter 11
Data.Pattern.Recognition.Based.on.Ultra-High.Frequency.Sigmoid.and.
Trigonometric.Higher.Order.Neural.Networks...................................................455
Chapter 12
Face.Recognition.Based.on.Higher.Order.Neural.Network.Group-Based.
Adaptive.Tolerance.Trees...................................................................................498
About the Author..............................................................................................537
Index...................................................................................................................538
Detailed Table of Contents
Preface.................................................................................................................xiii
Acknowledgment...............................................................................................xxii
Section 1
Models of Artificial Higher Order Neural Networks
Chapter 1
Models.of.Artificial.Higher.Order.Neural.Networks..............................................1
This.chapter.introduces.the.background.of.the.higher.order.neural.network.(HONN).
model.developing.history.and.overviews.24.applied.artificial.higher.order.neural.
network.models..This.chapter.provides.24.HONN.models.and.uses.a.single.uniform.
HONN.architecture.for.all.24.HONN.models..This.chapter.also.uses.a.uniform.learning.
algorithm.for.all.24.HONN.models.and.uses.uniform.weight.update.formulae.for.
all.24.HONN.models..In.this.chapter,.polynomial.HONN,.Trigonometric.HONN,.
Sigmoid.HONN,.SINC.HONN,.and.Ultra.High.Frequency.HONN.structure.and.
models.are.overviewed.too.
Chapter 2
Models.of.Artificial.Multi-Polynomial.Higher.Order.Neural.Networks..............97
This.chapter.introduces.multi-polynomial.higher.order.neural.network.models.
(MPHONN).with.higher.accuracy..Using.Sun.workstation,.C++,.and.Motif,.a.
MPHONN.simulator.has.been.built..Real-world.data.cannot.always.be.modeled.
simply.and.simulated.with.high.accuracy.by.a.single.polynomial.function..Thus,.
ordinary.higher.order.neural.networks.could.fail.to.simulate.complicated.real-
world.data..But.MPHONN.model.can.simulate.multi-polynomial.functions.and.can.
produce.results.with.improved.accuracy.through.experiments..By.using.MPHONN.
for.financial.modeling.and.simulation,.experimental.results.show.that.MPHONN.
can.always.have.0.5051%.to.0.8661%.more.accuracy.than.ordinary.higher.order.
neural.network.models.

Chapter 3
Group.Models.of.Artificial.Polynomial.and.Trigonometric.Higher.Order.
Neural.Networks.................................................................................................137
Real-world.financial.data.is.often.discontinuous.and.non-smooth..Neural.network.
group.models.can.perform.this.function.with.more.accuracy..Both.polynomial.
higher.order.neural.network.group.(PHONNG).and.trigonometric.polynomial.
higher.order.neural.network.group.(THONNG).models.are.studied.in.this.chapter..
These.PHONNG.and.THONNG.models.are.open.box,.convergent.models.capable.
of.approximating.any.kind.of.piecewise.continuous.function,.to.any.degree.of.
accuracy..Moreover,.they.are.capable.of.handling.higher.frequency,.higher.order.
nonlinear,.and.discontinuous.data..Results.confirm.that.PHONNG.and.THONNG.
group.models.converge.without.difficulty.and.are.considerably.more.accurate.
(0.7542%.-.1.0715%).than.neural.network.models.such.as.using.polynomial.higher.
order.neural.network.(PHONN).and.trigonometric.polynomial.higher.order.neural.
network.(THONN).models.
Section 2
Artificial Higher Order Neural Networks for Economics and
Business
Chapter 4
SAS.Nonlinear.Models.or.Artificial.Higher.Order.Neural.Network.Nonlinear.
Models?...............................................................................................................174
This.chapter.delivers.general.format.of.higher.order.neural.networks.(HONNs).
for.nonlinear.data.analysis.and.six.different.HONN.models..Then,.this.chapter.
mathematically.proves.that.HONN.models.could.converge.and.have.mean.squared.
errors.close.to.zero..Moreover,.this.chapter.illustrates.the.learning.algorithm.with.
update.formulas..HONN.models.are.compared.with.SAS.nonlinear.(NLIN).models,.
and.results.show.that.HONN.models.are.3.to.12%.better.than.SAS.nonlinear.models..
Finally,.this.chapter.shows.how.to.use.HONN.models.to.find.the.best.model,.order,.
and.coefficients.without.writing.the.regression.expression,.declaring.parameter.
names,.and.supplying.initial.parameter.values.
Chapter 5
Time.Series.Data.Analysis.by.Ultra-High.Frequency.Trigonometric.Higher.
Order.Neural.Networks.......................................................................................218
This.chapter.develops.a.new.nonlinear.model,.ultra.high.frequency.trigonometric.
higher.order.neural.networks.(UTHONN).for.time.series.data.analysis..UTHONN.
includes.three.models:.UCSHONN.(ultra.high.frequency.sine.and.cosine.higher.
order.neural.networks).models,.UCCHONN.(ultra.high.frequency.cosine.and.
cosine.higher.order.neural.networks).models,.and.USSHONN.(ultra.high.frequency.