Soft Computing (SC), an innovative approach to constructing computationally intelligent systems, has just come into the limelight. It is now realized that complex real-world problems require intelligent systems that combine knowledge techniques, and methodologies from various sources. These intelligent systems are supposed to possess human like expertise within a specific domain, adopt themselves and learn to do better in changing environments, and explain how they make decisions or take actions. In confronting real-world computing problems, it is frequently advantageous to use several computing techniques synergistically rather than exclusively resulting in construction of complementary hybrid intelligent systems.

The quintessence of designing intelligent systems of this kind is neuron-fuzzy computing: neural networks that recognize patterns and adopt themselves to cope with changing environments; fuzzy inference systems that incorporate human knowledge and perform inferencing and decision-making. The integration of these two complementary approaches, together with certain derivative free optimization techniques, results in neuron-fuzzy & soft computing.

The papers in this book are the refereed papers which will help the students and software practitioners to update their knowledge in the field of Soft Computing.