Leszek Rutkowski Livres

This book explores the integration of neural networks and fuzzy logic to create adaptive systems capable of handling uncertainty and imprecision. It discusses the theoretical foundations of neuro-fuzzy systems and their applications in various fields, including control systems, data mining, and pattern recognition. The text emphasizes the flexibility of these systems in learning from data and improving performance over time, making it a valuable resource for researchers and practitioners interested in advanced computational intelligence techniques.

Focusing on the integration of interdisciplinary engineering ideas, this volume highlights the concept of soft computing as essential for developing human-friendly technology in the twenty-first century. It showcases the pioneering work of Professor Rutkowski, particularly in probabilistic neural networks, emphasizing Bayesian properties for effective regression and classification in dynamic environments. The book combines theoretical insights with practical applications, marking a significant contribution to the fields of information science and engineering.

The volume LNCS 7269 constitutes the refereed proceedings of the International Symposium on Swarm Intelligence and Differential Evolution, SIDE 2012, held in Zakopane, Poland, in April/May 2012 in conjunction with the 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2012 (proceedings published as two-volume set LNAI 7267 and 7268). The 212 revised full papers presented were carefully reviewed and selected from 483 submissions. The volume is divided into two topical parts: proceedings of the 2012 symposium on swarm intelligence and differential evolution and on evolutionary algorithms and their applications.

The publication of this book marks a significant event, addressing a critical gap in domestic science and technical literature. It introduces readers to the latest advancements in computational intelligence, a rapidly evolving field that has gained prominence in recent years. This area of study is vital for processing information from data and providing meaningful interpretations based on user programming. The last few decades have witnessed remarkable progress in computer techniques and related computational methods, which have propelled both theoretical and applied sciences forward. This advancement allows users to fully leverage the newly created computational potential and extract knowledge from an ever-growing wealth of data. The evolution of computational intelligence is closely linked to the increase in available data and the capabilities for processing it, with these factors being mutually supportive. Without them, the field's growth would be nearly impossible, and its applications would be limited. Consequently, these techniques have seen significant development recently. Inspired by the observable and replicable aspects of human and natural intelligence, computational intelligence systems emulate how nature processes data in parallel, regulating actions through feedback mechanisms.

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, held in Zakopane, Poland in June 2004. The 172 revised contributed papers presented together with 17 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on neural networks, fuzzy systems, evolutionary algorithms, rough sets, soft computing in classification, image processing, robotics, multiagent systems, problems in AI, intelligent control, modeling and system identification, medical applications, mechanical applications, and applications in various fields.

Science has advanced significantly in the twentieth century, establishing proper disciplines in fields like physics, computer science, and molecular biology. Alongside this progress, interdisciplinary engineering ideas have emerged, such as artificial intelligence, fuzzy logic, and data mining. To create truly human-friendly technology in the twenty-first century, it is essential to integrate various methods beyond traditional disciplines. Soft computing plays a crucial role in developing such technology for our modern information society. Professor Rutkowski, a pioneer in this field, has dedicated years to publishing a wide range of original work. This volume, primarily based on his research, marks a significant milestone in soft computing by integrating diverse disciplines from information science and engineering. The book is divided into three parts, with the first focusing on probabilistic neural networks. As neural excitation is stochastic, it is logical to explore the Bayesian properties of the connectionist structures developed by Rutkowski. This innovative approach has proven particularly effective for addressing regression and classification challenges in dynamic environments. Throughout the book, key themes are drawn from theoretical subjects closely linked to practical applications.