Preface to the Special Issue on Neural Networks, Fuzzy Logic, and Evolutionary Computing for Intelligent System Design

Soft computing can be used to build hybrid intelligent systems for achieving different goals in real-world applications. Soft Computing techniques include, at the moment, fuzzy logic, neural networks, genetic algorithms, chaos theory methods, and similar techniques that have been proposed in recent years. Each of these techniques has advantages and disadvantages, and several real-world problems have been solved, by using one of these techniques. However, many real-world complex problems require the integration of several of these techniques to really achieve the efficiency and accuracy needed in practice. In particular, evolutionary computing can be used to optimize the topology of a fuzzy or a neural system. Also, there are neuro-fuzzy approaches or even neuro-fuzzy-genetic approaches for designing the best intelligent system for a particular application. This special issue consists of fourteen papers that consider the use and integration of different soft computing techniques for the development of hybrid intelligent systems for modeling, simulation and control of non-linear dynamical systems. The ten papers, of this special issue, describe different applications of soft computing techniques to real-world problems and can be considered a significant contribution to the field of hybrid intelligent systems. The first paper, " A Fuzzy Clustering Approach for Face Recognition based on Face Feature Lines and Eigenvectors " by Mario Chacon et al., deals with a new approach using fuzzy clustering for face recognition. The new approach is based on face feature lines and eigenvectors. To achieve good performance the best combination of methods has to be defined very carefully. Experimental results show the suitability of the architecture and effectiveness of the proposed intelligent approach. The second paper, " Fuzzy Inference Systems Type-1 and Type-2 for Digital Images Edge Detection " by Olivia Mendoza et al., describes a fuzzy logic approach for edge detection in images. The method is applied to achieve the goal of making edge detection a soft decision process, in this way allowing uncertainty management. Type-2 fuzzy logic can handle a higher degree of uncertainty than type-1 fuzzy logic. Comparative simulation results confirmed the applicability of type-2 fuzzy logic. The third paper, " One-Dimensional Kohonen Networks and their Application to Automatic Classification of Images " by Ricardo Perez-Aguila et al., describes the use of neural network approach for the classification of images in an automated way. Human experts perform the classification of images and neural networks can be used to learn to do this task. In this …