Tijuana Institute of Technology

Academic Research Group

Hybrid Intelligent Systems

Motivation for the Academic Group

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 Academic Group 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 Academic Group, 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 Academic Group. 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.



Oscar Castillo, Tijuana Institute of Technology, Mexico

Ocastillo –at- tectijuana.mx

Webpage: www.hafsamx.org/castillo


Patricia Melin, Tijuana Institute of Technology, Mexico


Webpage: www.hafsamx.org/melin