Preface to the Special Issue on Hybrid Intelligent Systems using Neural Networks, Fuzzy Logic, and Genetic Algorithms

Soft computing can be used to build hybrid intelligent systems for achieving different goals in real-world applications. Soft Computing (SC) 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, genetic algorithms 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. With all of this in mind, we did organize this Special Issue to show the most recent research results of this type of work. This special issue consists of ten 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, " The Communication in Intelligent Agents for Distributed Fault Tolerant Systems " by Arnulfo Alanis et al., deals with a new approach using intelligent agents to design and implement distributed fault tolerant systems. To achieve good performance the communication between agents has to be defined very carefully. Experimental results show the suitability of the architecture and effectiveness of the proposed intelligent approach. The second paper, " An Integral Plus States Adaptive Neural Control of Aerobic Continuous Stirred Tank Reactor " by Ieroham Baruch et al., describes an adaptive neural network control structure to regulate a biological fermentation processes. The method is applied to achieve the goal of keeping the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations and measurement noise. Comparative simulation results confirmed the applicability of the proposed control scheme. The third paper, " Approximate Method for Concrete Mix Design: A Layered Fuzzy-Neuro System Model " by M. C. Nataraja et al., describes the use of fuzzy-neuro approach for concrete mix design. Human experts perform the design of concrete …