Patricia Melin Libros

Soft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. This book describes the application of soft computing techniques to intelligent control, pattern recognition, and optimization problems. The book is organized in four main parts. The first part deals with nature-inspired optimization methods and their applications. Papers included in this part propose new models for achieving intelligent optimization in different application areas. The second part discusses hybrid intelligent systems for achieving control. Papers included in this part make use of nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers for different kind of applications. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems. The fourth part discusses new theoretical concepts and methods for the application of soft computing to many different areas, such as natural language processing, clustering and optimization.

This book features a selection of papers from IFSA 2007, focusing on innovative methods for analyzing and designing hybrid intelligent systems through soft computing techniques. Soft Computing (SC) encompasses various paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be leveraged to create robust hybrid systems for addressing challenges in pattern recognition, time series prediction, intelligent control, robotics, and automation. Given the complexity and high dimensionality of real-world problems, hybrid intelligent systems that integrate multiple SC techniques are essential. The design of these systems' architectures significantly influences their efficiency and accuracy, making optimization crucial. Various architectures can combine neural networks, fuzzy logic, and genetic algorithms in diverse ways to achieve goals in pattern recognition, time series prediction, and other applications. This book serves as a key reference for scientists and engineers eager to utilize new computational and mathematical tools for designing hybrid intelligent systems. Additionally, it is suitable for graduate courses in soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and related fields. Organized into twelve main parts, the book groups papers by common subjects for easier navigation.

This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.

The book explores innovative methods for developing intelligent systems through type-2 fuzzy logic and soft computing techniques. It emphasizes the integration of type-2 fuzzy logic with traditional approaches like neural networks and genetic algorithms to enhance hybrid systems. Key applications discussed include real-world pattern recognition challenges such as face, fingerprint, and voice recognition, as well as intelligent control and manufacturing. Aimed at scientists and engineers, it serves as a comprehensive reference for applying these advanced techniques in various fields.

Focusing on type-3 fuzzy logic, this book explores its application in time series prediction, emphasizing its superiority in managing uncertainty for enhanced results. It integrates neural networks and fractal theory, presenting various hybrid intelligent methods tested on real-world prediction challenges such as COVID-19 and stock market trends. Aimed at scientists and engineers, it serves as a comprehensive reference for graduate courses in soft computing and related fields, while also inspiring new research avenues in intelligent prediction methodologies.

This book comprises a selection of papers from IFSA 2007 on new methods and theories that contribute to the foundations of fuzzy logic and soft computing. These papers were selected from over 400 submissions and constitute an imp- tant contribution to the theory and applications of fuzzy logic and soft c- puting methodologies. Soft computing consists of several computing paradigms, including fuzzy logic, neural networks, genetic algorithms, and other techniques, which can be used to produce powerful intelligent systems for solving real-world problems. The papers of IFSA 2007 also make a contribution to this goal. This book is intended to be a major reference for scientists and engineers interested in applying new computational and mathematical tools to achieve intelligent solutions to complex problems. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the papers contained in the book. The book is divided into 14 main parts. Eachpart contains a set of papers on a common subject, so that the reader can ? nd similar papers grouped together. Some of these parts comprise the papers of organized sessions of IFSA 2007 and we thank the session organizers for their incredible job in forming these sessions with invited and regular paper submissions.

This book presents new methods for intelligent manufacturing through the integration of soft computing techniques and fractal theory. Soft Computing (SC) encompasses various paradigms, including fuzzy logic, neural networks, and genetic algorithms, enabling the creation of powerful hybrid intelligent systems. Fractal theory offers mathematical tools to comprehend the geometrical complexity of natural objects, aiding in identification and modeling. By combining SC techniques with fractal theory, we harness the intelligence of computational methods alongside the descriptive power of fractals. Industrial manufacturing systems are inherently non-linear dynamical systems, exhibiting complex dynamic behaviors, highlighting the necessity for computational intelligence in these environments. We define "intelligent manufacturing" as the application of SC techniques and fractal theory to achieve key manufacturing objectives, such as production planning and control, fault monitoring and diagnosis, and automated quality control. The book begins with an overview of existing methodologies in Soft Computing, followed by a detailed description of our approach to addressing challenges in intelligent manufacturing. Our perspective emphasizes that achieving intelligent manufacturing in practical applications requires the integration of SC techniques and fractal theory.

This book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications. Control of non-linear dynamical systems cannot be achieved if we don't have the appropriate model for the system. On the other hand, we know that complex non-linear dynamical systems can exhibit a wide range of dynamic behaviors ( ranging from simple periodic orbits to chaotic strange attractors), so the problem of simulation and behavior identification is a very important one. Also, we want to automate each of these tasks because in this way it is more easy to solve a particular problem. A real world problem may require that we use modelling, simulation, and control, to achieve the desired level of performance needed for the particular application.