Artificial Neural Networks and Type-2 Fuzzy Set
Elements of Soft Computing and Its Applications
- 1st Edition - February 19, 2025
- Imprint: Morgan Kaufmann
- Authors: Snehashish Chakraverty, Arup Kumar Sahoo, Dhabaleswar Mohapatra
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 2 8 9 4 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 2 8 9 5 - 4
Soft computing is an emerging discipline which aims to exploit tolerance for imprecision, approximate reasoning, and uncertainty to achieve robustness, tractability, and cost… Read more
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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.
- Covers the fundamental concepts and the latest research on variants of Artificial Neural Networks, including scientific machine learning and Type-2 Fuzzy Set
- Discusses the integration of ANN and Type-2 Fuzzy Set, showing how combining these two approaches can enhance the performance and robustness of intelligent systems
- Demonstrates how to solve scientific and engineering research problems through a variety of real-world examples and case studies
- Includes coverage of both static and dynamic problems, along with validation of ANN and Fuzzy models by comparing the obtained solutions of each model with already existing solutions that have been obtained with numerical or analytical methods
Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts, software engineers, as well as researchers and professionals across the fields of science and engineering
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
- Edition: 1
- Published: February 19, 2025
- Imprint: Morgan Kaufmann
- Language: English
SC
Snehashish Chakraverty
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.
Affiliations and expertise
HAG Professor, Department of Mathematics, Applied Mathematics Group, National Institute of Technology Rourkela, Rourkela, Odisha, India. Differential Equations (ordinary, partial, and fractional), Numerical Analysis, Computational Methods, Structural Dynamics (FGM, Nano), Fluid Dynamics, Mathematical and Uncertainty Modelling, Soft Computing and Machine Intelligence (Artificial Neural Network, Fuzzy, Interval, and Affine Computations).AS
Arup Kumar Sahoo
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.
Affiliations and expertise
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.DM
Dhabaleswar Mohapatra
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.
Affiliations and expertise
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.Read Artificial Neural Networks and Type-2 Fuzzy Set on ScienceDirect