Applications of Artificial Neural Networks for Nonlinear Data

Applications of Artificial Neural Networks for Nonlinear Data

Copyright: © 2021 |Pages: 315
ISBN13: 9781799840428|ISBN10: 1799840425|EISBN13: 9781799840435
DOI: 10.4018/978-1-7998-4042-8
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MLA

Patel, Hiral Ashil, and A.V. Senthil Kumar, editors. Applications of Artificial Neural Networks for Nonlinear Data. IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4042-8

APA

Patel, H. A. & Kumar, A. (Eds.). (2021). Applications of Artificial Neural Networks for Nonlinear Data. IGI Global. https://doi.org/10.4018/978-1-7998-4042-8

Chicago

Patel, Hiral Ashil, and A.V. Senthil Kumar, eds. Applications of Artificial Neural Networks for Nonlinear Data. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4042-8

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Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient.

Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

Table of Contents

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Front Materials
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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Editorial Advisory Board
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Preface
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Acknowledgment
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Chapters
Chapter 1
Concepts and Categorization
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Chapter 2
Applications and Analytics
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Back Materials
Compilation of References
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About the Contributors
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Index
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