Soft Computing Approach for Mathematical Modeling of Engineering Problems

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5. Industry 4.0
advanced soft computing for engineering problems
Augmented Reality (AR)
Automated Guided Vehicle
CA
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Category=UB
control theory applications
Convolutional Neural Networks
Cryptosystem
Cyber Physical Systems
decision making algorithms
dynamic system analysis
Elimination of Choice Expressing Reality (ELECTRE) method
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error propagation modeling
Fourth Industrial Revolution
Fractional Order Chaotic System
Fuzzy Environment
Fuzzy Neural Network Model
Fuzzy Set
Fuzzy Set Theory
Fuzzy System
Hesitant Fuzzy Sets
Hyperparameter Tuning
Interval Number
Intuitionistic Fuzzy Sets
Membership Functions
Mobile AR Application
Multi-layer Perceptron Neural Network
nonsingular kernel derivative
numerical simulation methods
PSO
RGB Color Space
RSA Cryptosystem
RSA Modulus
Soft Computing
Soft Computing Tools
Swarm Intelligence
Triangular Fuzzy Numbers
UACI
uncertainty quantification
UV Map

Product details

  • ISBN 9780367685997
  • Weight: 570g
  • Dimensions: 156 x 234mm
  • Publication Date: 03 Sep 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds
• Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models
• Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions
• Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behavior
This book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems.

Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.
Soheil Salahshour is an associate professor at Bahcesehir University.

Ali Ahmadian is visiting Assistant Professor at Mediterranea University of Reggio Calabria and Senior Fellow Researcher at Institute of IR 4.0, The National University of Malaysia.

Soheil Salahshour is an Associate Professor at Bahcesehir University.