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 five 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 nonlinear dynamical systems. The five 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, "An Artificial Bee Hive for Continuous Optimization" by Mario A. MuΓ±oz et al., deals with an artificial bee-hive algorithm for optimization in continuous search spaces based on a model aimed at individual bee behavior. The algorithm defines a set of behavioral rules for each agent to determine what kind of actions must be carried out. In addition, the proposed algorithm includes some adaptations not considered in the biological model to increase the performance in the search for better solutions. To compare the performance of the algorithm to other swarm-based algorithms a statistical analysis was performed.
The second paper, "A Levenberg-Marquardt Learning Applied for Recurrent Neural Identification and Control for a Wastewater Treatment Bioprocess" by Ieroham Baruch and Carlos R. Mariaca-Gaspar, describes a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive