Unlike traditional computing, Computational Intelligence is tolerant of imprecise information, partial truth and uncertainty. This book presents a selected collection of contributions on a focused treatment of important elements of CI, centred on its key element: learning. All the contributors of this volume have direct bearing with this issue. From fundamentals to advanced systems as Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Radial Basis Function Networks (RBF) and its relations with Fuzzy Sets and Support Vector Machines theory; and on to several critical applications in Engineering and Manufacturing. These are among applications where CI have excellent potential.

This volume has specially taken Neural Networks, key elements of CI, to the next level. Both novice and expert readers can benefit from this timely addition to CI based literature. Towards that goal, the editors and the authors have made critical contributions and succeeded. They have paved the road for learning paradigms towards the solution of many real-world problems.