книга Soft Computing Approach for Mathematical Modeling of Engineering Problems

А Б В Г Д Е Ж З И К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Э Ю Я
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Soft Computing Approach for Mathematical Modeling of Engineering Problems

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Название: Soft Computing Approach for Mathematical Modeling of Engineering Problems
Автор: Ali Ahmadian, Soheil Salahshour
Страниц: 204
Формат: EPUB
Размер: 10.17 MB
Качество: Отличное
Язык: Английский
Год издания: 2022


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.



Contents:

Chapter 1 Soft Computing Techniques: An Overview

1.1 Introduction

1.2 The Concept of Uncertainty: The Role of Fuzzy Logic

1.3 The Concept of Complexity: The Role of Artificial Neural Networks

1.4 The Concept of Optimization: The Role of Evolutionary Algorithms

1.5 Concluding Remarks

Chapter 2 Solution of Linear Difference Equation in Interval Environment and Its Application

2.1 Introduction

2.2 Preliminaries

2.3 Flowchart of Solution Approach

2.4 Difference Equation with Interval Environment

2.5 Numerical Example and Application

2.6 Conclusion

Chapter 3 Industrial Internet of Things and Industry 4.0

Chapter 4 Industry 4.0 and Its Practice in Terms of Fuzzy Uncertain Environment

Chapter 5 Consistency of Aggregation Function-Based m-Polar Fuzzy Digraphs in Group Decision Making

Chapter 6 Path Programming Problems in Fuzzy Environment

Chapter 7 Weather Forecast and Climate Prediction Using Soft Computing Methods

7.2 Artificial Neural Networks

7.2.1 A Concise Introduction to ANNs

Chapter 8 Color Descriptor for Mobile Augmented Reality

Chapter 9 Cryptosystem for Meshed 3D through Cellular Automata

Chapter 10 Evolutionary Computing and Swarm Intelligence for Hyper Parameters Optimization Problem in Convolutional Neural Networks

10.1.1 Bayesian Optimization

10.2 Deep Learning Overview

Chapter 11 New Approach for Efficiently Computing Factors of the RSA Modulus

Chapter 12 Vision-Based Efficient Collision Avoidance Model Using Distance Measurement

Index


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