Soft Numerical Computing in Uncertain Dynamic Systems

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  • Author : Tofigh Allahviranloo
  • Publisher : Academic Press
  • Pages : 388 pages
  • ISBN : 0128229942
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Soft Numerical Computing in Uncertain Dynamic Systems

Download or Read online Soft Numerical Computing in Uncertain Dynamic Systems full in PDF, ePub and kindle. this book written by Tofigh Allahviranloo and published by Academic Press which was released on 01 September 2020 with total page 388 pages. We cannot guarantee that Soft Numerical Computing in Uncertain Dynamic Systems book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods—including convergence and consistence properties and characteristics—and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students. One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily—sometimes they are impossible to solve. Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix. Explores dynamic models, how time is fundamental to the structure of the model and data, and how a process unfolds Investigates the dynamic relationships between multiple components of a system in modeling using mathematical models and the concept of stability in uncertain environments Exposes readers to many soft numerical methods to simulate the solution function’s behavior

Soft Numerical Computing in Uncertain Dynamic Systems

Soft Numerical Computing in Uncertain Dynamic Systems
  • Author : Tofigh Allahviranloo,Witold Pedrycz
  • Publisher : Academic Press
  • Release : 01 September 2020
GET THIS BOOK Soft Numerical Computing in Uncertain Dynamic Systems

Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods—including convergence and consistence properties and characteristics—and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students

Soft Computing Approach for Mathematical Modeling of Engineering Problems

Soft Computing Approach for Mathematical Modeling of Engineering Problems
  • Author : Ali Ahmadian,Soheil Salahshour
  • Publisher : CRC Press
  • Release : 03 September 2021
GET THIS BOOK Soft Computing Approach for Mathematical Modeling of Engineering Problems

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

Mathematical Methods in Interdisciplinary Sciences

Mathematical Methods in Interdisciplinary Sciences
  • Author : Snehashish Chakraverty
  • Publisher : John Wiley & Sons
  • Release : 02 June 2020
GET THIS BOOK Mathematical Methods in Interdisciplinary Sciences

Brings mathematics to bear on your real-world, scientific problems Mathematical Methods in Interdisciplinary Sciences provides a practical and usable framework for bringing a mathematical approach to modelling real-life scientific and technological problems. The collection of chapters Dr. Snehashish Chakraverty has provided describe in detail how to bring mathematics, statistics, and computational methods to the fore to solve even the most stubborn problems involving the intersection of multiple fields of study. Graduate students, postgraduate students, researchers, and professors will all benefit

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
  • Author : Yuri Tiumentsev,Mikhail Egorchev
  • Publisher : Academic Press
  • Release : 17 May 2019
GET THIS BOOK Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions

Soft Computing Based Nonlinear Control Systems Design

Soft Computing Based Nonlinear Control Systems Design
  • Author : Singh, Uday Pratap,Tiwari, Akhilesh,Singh, Rajeev Kumar
  • Publisher : IGI Global
  • Release : 09 February 2018
GET THIS BOOK Soft Computing Based Nonlinear Control Systems Design

A critical part of ensuring that systems are advancing alongside technology without complications is problem solving. Practical applications of problem-solving theories can model conflict and cooperation and aid in creating solutions to real-world problems. Soft-Computing-Based Nonlinear Control Systems Design is a critical scholarly publication that examines the practical applications of control theory and its applications in problem solving to fields including economics, environmental management, and financial modelling. Featuring a wide range of topics, such as fuzzy logic, nature-inspired algorithms, and

Uncertain Computation based Decision Theory

Uncertain Computation based Decision Theory
  • Author : Aliev Rafig Aziz
  • Publisher : World Scientific
  • Release : 06 December 2017
GET THIS BOOK Uncertain Computation based Decision Theory

Uncertain computation is a system of computation and reasoning in which the objects of computation are not values of variables but restrictions on values of variables. This compendium includes uncertain computation examples based on interval arithmetic, probabilistic arithmetic, fuzzy arithmetic, Z-number arithmetic, and arithmetic with geometric primitives. The principal problem with the existing decision theories is that they do not have capabilities to deal with such environment. Up to now, no books where decision theories based on all generalizations level

Handbook of Research on Novel Soft Computing Intelligent Algorithms

Handbook of Research on Novel Soft Computing Intelligent Algorithms
  • Author : Pandian Vasant
  • Publisher : IGI Global
  • Release : 31 August 2013
GET THIS BOOK Handbook of Research on Novel Soft Computing Intelligent Algorithms

"This book explores emerging technologies and best practices designed to effectively address concerns inherent in properly optimizing advanced systems, demonstrating applications in areas such as bio-engineering, space exploration, industrial informatics, information security, and nuclear and renewable energies"--Provided by publisher.

Intelligent Techniques and Soft Computing for Nuclear Science and Engineering

Intelligent Techniques and Soft Computing for Nuclear Science and Engineering
  • Author : Da Ruan
  • Publisher : World Scientific
  • Release : 06 December 2021
GET THIS BOOK Intelligent Techniques and Soft Computing for Nuclear Science and Engineering

This book is divided into three parts. The first part, ?Mathematical Tools and New Developments?, provides basic tools to treat fuzzy set theory, rough set theory, fuzzy control, fuzzy modelling, decision support systems, and related applications. The second part, ?Intelligent Engineering Applications?, reports on engineering problems such as man-machine interface, risk analysis, image processing, robotics, knowledge-based engineering, expert systems, process control integration, diagnosis, measurements and interpretation by intelligent techniques and soft computing used for general engineering applications. The third part, ?

Analysis and Design of Intelligent Systems Using Soft Computing Techniques

Analysis and Design of Intelligent Systems Using Soft Computing Techniques
  • Author : Patricia Melin,Oscar Castillo,Eduardo G. Ramírez,Witold Pedrycz
  • Publisher : Springer Science & Business Media
  • Release : 20 September 2007
GET THIS BOOK Analysis and Design of Intelligent Systems Using Soft Computing Techniques

This book comprises a selection of papers on new methods for analysis and design of hybrid intelligent systems using soft computing techniques from the IFSA 2007 World Congress, held in Cancun, Mexico, June 2007.

Affine Arithmetic Based Solution of Uncertain Static and Dynamic Problems

Affine Arithmetic Based Solution of Uncertain Static and Dynamic Problems
  • Author : Snehashish Chakraverty,Saudamini Rout
  • Publisher : Morgan & Claypool Publishers
  • Release : 01 April 2020
GET THIS BOOK Affine Arithmetic Based Solution of Uncertain Static and Dynamic Problems

Uncertainty is an inseparable component of almost every measurement and occurrence when dealing with real-world problems. Finding solutions to real-life problems in an uncertain environment is a difficult and challenging task. As such, this book addresses the solution of uncertain static and dynamic problems based on affine arithmetic approaches. Affine arithmetic is one of the recent developments designed to handle such uncertainties in a different manner which may be useful for overcoming the dependency problem and may compute better enclosures

Soft Computing and Fractal Theory for Intelligent Manufacturing

Soft Computing and Fractal Theory for Intelligent Manufacturing
  • Author : Oscar Castillo,Patricia Melin
  • Publisher : Springer Science & Business Media
  • Release : 22 January 2003
GET THIS BOOK Soft Computing and Fractal Theory for Intelligent Manufacturing

The book describes the application of soft computing techniques and fractal theory to intelligent manufacturing. Hybrid intelligent systems, which integrate different soft computing techniques and fractal theory, are also presented. The text covers the basics of fuzzy logic, neural networks, genetic algorithms, simulated annealing, chaos and fractal theory. It also describes in detail different hybrid architectures for developing intelligent manufacturing systems for applications in automated quality control, process monitoring and diagnostics, adaptive control of non-linear plants, and time series prediction.

Soft Computing and Its Applications

Soft Computing and Its Applications
  • Author : Rafik Aziz ogly Aliev,R. R. Aliev
  • Publisher : World Scientific
  • Release : 06 December 2021
GET THIS BOOK Soft Computing and Its Applications

The concept of soft computing is still in its initial stages of crystallization. Presently available books on soft computing are merely collections of chapters or articles about different aspects of the field. This book is the first to provide a systematic account of the major concepts and methodologies of soft computing, presenting a unified framework that makes the subject more accessible to students and practitioners. Particularly worthy of note is the inclusion of a wealth of information about neuro-fuzzy, neuro-genetic,

Advances in Soft Computing

Advances in Soft Computing
  • Author : Ildar Batyrshin,Grigori Sidorov
  • Publisher : Springer
  • Release : 22 November 2011
GET THIS BOOK Advances in Soft Computing

The two-volume set LNAI 7094 and 7095 constitutes the refereed proceedings of the 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The 96 revised papers presented were carefully selected from XXX submissions. The second volume contains 46 papers focusing on soft computing. The papers are organized in the following topical sections: fuzzy logic, uncertainty and probabilistic reasoning; evolutionary algorithms and other naturally-inspired algorithms; data mining; neural networks and hybrid intelligent systems; and computer vision and image processing.

Deep Learning and Neural Networks Concepts Methodologies Tools and Applications

Deep Learning and Neural Networks  Concepts  Methodologies  Tools  and Applications
  • Author : Management Association, Information Resources
  • Publisher : IGI Global
  • Release : 11 October 2019
GET THIS BOOK Deep Learning and Neural Networks Concepts Methodologies Tools and Applications

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and