Artificial Neural Networks and Type-2 Fuzzy Set
Elements of Soft Computing and Its Applications
Published on 23. May 2025
Book
Paperback/Softback
256 pages
978-0-443-32894-7 (ISBN)
Description
Soft computing is an emerging discipline which aims to exploit tolerance for imprecision, approximate reasoning, and uncertainty to achieve robustness, tractability, and cost effectiveness for building intelligent machines. Soft computing methodologies include neural networks, fuzzy sets, genetic algorithms, Bayesian networks, and rough sets, among others. In this regard, neural networks are widely used for modeling dynamic solvers, classification of data, and prediction of solutions, whereas fuzzy sets provide a natural framework for dealing with uncertainty. Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications covers the fundamental concepts and the latest research on variants of Artificial Neural Networks (ANN), including scientific machine learning and Type-2 Fuzzy Set (T2FS). In addition, the book also covers different applications for solving real-world problems along with various examples and case studies. It may be noted that quite a bit of research has been done on ANN and Fuzzy Set theory/ Fuzzy logic. However, Artificial Neural Networks and Type-2 Fuzzy Set is the first book to cover the use of ANN and fuzzy set theory with regards to Type-2 Fuzzy Set in static and dynamic problems in one place. Artificial Neural Networks and Type-2 Fuzzy Sets are two of the most widely used computational intelligence techniques for solving complex problems in various domains. Both ANN and T2FS have unique characteristics that make them suitable for different types of problems. This book provides the reader with in-depth understanding of how to apply these computational intelligence techniques in various fields of science and engineering in general and static and dynamic problems in particular. Further, for validation purposes of the ANN and fuzzy models, the obtained solutions of each model in the book is compared with already existing solutions that have been obtained with numerical or analytical methods.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
450 gr
ISBN-13
978-0-443-32894-7 (9780443328947)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Snehashish Chakraverty | Arup Kumar Sahoo | Dhabaleswar Mohapatra
Artificial Neural Networks and Type-2 Fuzzy Set
Elements of Soft Computing and Its Applications
E-Book
02/2025
Elsevier
€166.99
Available for download
Persons
Dr. Snehashish Chakraverty is a Senior Professor in the Department of Mathematics (Applied Mathematics Group), National Institute of Technology Rourkela, with over 30 years of teaching and research experience. A gold medalist from the University of Roorkee (now IIT Roorkee), he earned his Ph.D. from IIT Roorkee and completed post-doctoral work at the University of Southampton (UK) and Concordia University (Canada). He has also served as a visiting professor in Canada and South Africa. Dr. Chakraverty has authored/edited 38 books and published over 495 research papers. His research spans differential equations (ordinary, partial, fractional), numerical and computational methods, structural and fluid dynamics, uncertainty modeling, and soft computing techniques. He has guided 27 Ph.D. scholars, with 10 currently under his supervision.
He has led 16 funded research projects and hosted international researchers through prestigious fellowships. Recognized in the top 2% of scientists globally (Stanford-Elsevier list, 2020-2024), he has received numerous awards including the CSIR Young Scientist Award, BOYSCAST Fellowship, INSA Bilateral Exchange, and IOP Top Cited Paper Awards. He is Chief Editor of International Journal of Fuzzy Computation and Modelling and serves on several international editorial boards.
Arup Kumar Sahoo is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Siksha "O? Anusandhan (Deemed to be University), Odisha, India. He has joined as a postdoctoral research fellow at the Autonomous Navigation and Sensor Fusion Lab (ANSFL), The Hatter Department of Marine Technologies, University of Haifa, Israel. Dr. Sahoo holds a PhD from the Department of Mathematics, National Institute of Technology Rourkela, Odisha, India. He earned his MPhil in Mathematics from Utkal University, Bhubaneswar, Odisha, India, and MSc in Mathematics and Computing from Biju Patnaik University of Technology, Rourkela, Odisha, India. Dr Sahoo has authored and co-authored 13 research papers and book chapters published in journals and conferences. In 2023, he received the Best Paper Presenter Award at an IEEE Conference.
Dhabaleswar Mohapatra is currently working as an Assistant Professor in the Department of Mathematics at the Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India. He received his PhD from the National Institute of Technology, Rourkela, Odisha, India. To date, he has published 12 research articles in journals and book chapters.
He has led 16 funded research projects and hosted international researchers through prestigious fellowships. Recognized in the top 2% of scientists globally (Stanford-Elsevier list, 2020-2024), he has received numerous awards including the CSIR Young Scientist Award, BOYSCAST Fellowship, INSA Bilateral Exchange, and IOP Top Cited Paper Awards. He is Chief Editor of International Journal of Fuzzy Computation and Modelling and serves on several international editorial boards.
Arup Kumar Sahoo is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Siksha "O? Anusandhan (Deemed to be University), Odisha, India. He has joined as a postdoctoral research fellow at the Autonomous Navigation and Sensor Fusion Lab (ANSFL), The Hatter Department of Marine Technologies, University of Haifa, Israel. Dr. Sahoo holds a PhD from the Department of Mathematics, National Institute of Technology Rourkela, Odisha, India. He earned his MPhil in Mathematics from Utkal University, Bhubaneswar, Odisha, India, and MSc in Mathematics and Computing from Biju Patnaik University of Technology, Rourkela, Odisha, India. Dr Sahoo has authored and co-authored 13 research papers and book chapters published in journals and conferences. In 2023, he received the Best Paper Presenter Award at an IEEE Conference.
Dhabaleswar Mohapatra is currently working as an Assistant Professor in the Department of Mathematics at the Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India. He received his PhD from the National Institute of Technology, Rourkela, Odisha, India. To date, he has published 12 research articles in journals and book chapters.
Author
Assistant Professor, Department of Computer Science and Engineering, Siksha "O? Anusandhan (Deemed to be University), Odisha, India. Artificial Intelligence, Scientific Machine Learning, Artificial Neural Networks, Advanced Navigation Algorithm, Autonomous Vehicles and Differential Equations.
Assistant Professor, Department of Mathematics, Siksha "O? Anusandhan (Deemed to be University), Odisha, India. Type-2 Fuzzy Uncertainty, Static and Dynamic Problems with Uncertainty, Numerical and Analytical Methods, and Fuzzy Differential and Fractional Differential Equations.
Content
1. Introduction to Soft Computing
Part I: Artificial Neural Network
2. Artificial Neural Network: An Overview
3. Mathematical Formulation of Neural network for Differential Equations
4. Recent Trends in Activation Functions for Solving Differential Equations
5. Curriculum Learning for Artificial Neural Network
6. Symplectic Artificial Neural Network
7. Wavelet Neural Network
8. Physics Informed Neural Network
Part II: Type-2 Fuzzy Uncertainty
9. Fuzzy Set Theory: An Overview
10. Preliminaries of Type-2 Fuzzy Set
11. Uncertain Static Engineering Problems
12. Linear Dynamical Problems with Uncertainty
13. Non-Linear Dynamical Problems with Uncertainty
14. Type-2 Fuzzy Initial Value Problems with Applications
15. Type-2 Fuzzy Fractional Differential Equations with Applications
Part I: Artificial Neural Network
2. Artificial Neural Network: An Overview
3. Mathematical Formulation of Neural network for Differential Equations
4. Recent Trends in Activation Functions for Solving Differential Equations
5. Curriculum Learning for Artificial Neural Network
6. Symplectic Artificial Neural Network
7. Wavelet Neural Network
8. Physics Informed Neural Network
Part II: Type-2 Fuzzy Uncertainty
9. Fuzzy Set Theory: An Overview
10. Preliminaries of Type-2 Fuzzy Set
11. Uncertain Static Engineering Problems
12. Linear Dynamical Problems with Uncertainty
13. Non-Linear Dynamical Problems with Uncertainty
14. Type-2 Fuzzy Initial Value Problems with Applications
15. Type-2 Fuzzy Fractional Differential Equations with Applications