Soft Numerical Computing in Uncertain Dynamic Systems

One of the important topics in applied science is dynamic systems and their applications. If these systems are involved with complex-uncertain data then they will be more important and practical; real-life problems work with this type of data and most of them cannot be solved exactly and easily and sometimes they are impossible to solve. In Soft Numerical Computing in Uncertain Dynamic Systems, the authors develop these models and deliver solutions to them with the aid of numerical methods. Since they are inherently uncertain, so uncertain and soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. Clearly, all the numerical methods need to consider error of approximation. Having this in mind, the books aims to discuss several types of errors and their propagation. Moreover, numerical methods complete with convergence and consistence properties and characteristics, so the other main objectives involve considerations, discussion and proving 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 along with their pertinent computing. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition; they can benefit from the 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. 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 in control and finite element fields as well as all engineering, applied mathematics, economics, and computer science, interested in dynamic and uncertain systems. Graduate courses offered at MSc and PhD level in applied sciences like control and optimal control include uncertain dynamic systems. Explores dynamic models, how time is fundamental to the structure of the model and data, as well as the understanding of how a process unfoldsInvestigates the dynamic relationships between multiple components of a system in modeling using mathematical models and the concept of stability in uncertain environmentExposes readers to many soft numerical methods to simulate the solution function's behavior INDICE: 1. Introduction2. Uncertain Sets3. Soft Computing with Uncertain Set4. Continuous Numerical Solution of Uncertain Differential Equations5. Discrete Numerical Solution of Uncertain Differential Equations6. Numerical Solution of Uncertain Fractional Differential Equations7. Numerical Solution of Uncertain Partial Differential Equations

• ISBN: 978-0-12-822855-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 320
• Fecha Publicación: 01/11/2020
• Nº Volúmenes: 1
• Idioma: Inglés