IEEE Computational Intelligence Society

Neural Network Technical Committee

Task Force on Hybrid Intelligent Systems

Motivation for the Task Force

There is a need of investigating hybrid approaches combining neural networks and fuzzy logic with other intelligent methodologies. For this reason we consider that it is appropriate to provide a forum for the interaction between the neural network community and the different communities in computational intelligence (like fuzzy logic and evolutionary computing). This Task Force will contribute to the integration of different Soft Computing (SC) methodologies for the development of hybrid intelligent systems for modeling, simulation and control of non-linear dynamical systems. SC methodologies at the moment include (at least) Neural Networks, Fuzzy Logic, Genetic Algorithms and Chaos Theory. Each of these methodologies has advantages and disadvantages and many problems have been solved, by using one of these methodologies. However, many real-world complex problems require the integration of several of these methodologies to really achieve the efficiency and accuracy needed in practice. In this Task Force, research on all SC methodologies will be considered, and their applications to modeling, simulation and control, will also be given careful consideration. Detailed methods for integrating the different SC methodologies in solving real-world problems will also be considered by the members of the Task Force. Hybrid intelligent systems with applications on the following areas will be considered: Robotic Dynamic Systems, Non-linear Plants, Manufacturing Systems, Pattern Recognition and Time Series Prediction


Chair: Dr. Eduardo Gomez-Ramirez (Mexico)

Vice-Chair: Prof. Patricia Melin (Mexico)


   Prof. Oscar Castillo (Mexico)

   Prof. Janusz Kacprzyk (Poland)

   Prof. Witold Pedrycz (Canada)

   Dr. Roseli Francelin-Romero (Brazil)

   Dr. Juan R Castro (Mexico)

   Prof. Sanghamitra Bandyopadhyay (India)

   Dr. Pilar Gomez-Gil (Mexico)