Soft Computing Approach for Mathematical Modeling of Engineering Problems

This book describes the use of different mathematical modeling and soft computing techniques used to solve real world engineering problems. The book 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 real word 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 real-life 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 consider error of approximation and discuss several common types of errors and their propagations. Moreover, they explain the numerical methods, complete with convergence and consistence properties and characteristics, so the main objectives involve considerations, discussion and proving related theorems within the setting of soft computing.

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.