Theory and Applications of Fuzzy Systems and Neural Networks

Fuzzy systems and neural networks are the main theoretical approaches in computational intelligence. These approaches have been successfully applied in a wide range of fields, such as information science, mathematics, control engineering, image processing, pattern recognition, robotics, mechatronics, consumer electronics, and system optimisation. They provide an effective tool for data and knowledge-based modelling that deals with many real-world problems with quantitative and qualitative complexity in terms of dimensionality and uncertainty. Fuzzy systems and neural networks complement each other and can be combined with other computational and artificial intelligence-based techniques, such as evolutionary algorithms and machine learning, to solve complex real-world problems. The integration of fuzzy systems and neural networks, in particular, can bring out the best of both approaches and usually provides better system performances in terms of modelling efficiency and accuracy. This reprint features original research of the highest scientific quality related to the theory and applications of fuzzy systems and neural networks. It includes original and unpublished works that present innovative methods for enhancing fuzzy systems and neural networks. Its scope includes systematic and empirical studies that contribute to significant novel developments in the theoretical and applied aspects of research in the field.