Analysis and design of intelligent systems using soft computing techniques

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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.