A forecasting solution to the oil spill problem based on a hybrid intelligent system
Information Sciences: an International Journal
Maximum likelihood hebbian learning based Retrieval method for CBR systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
An IBR system to quantify the ocean’s carbon dioxide budget
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
A beta-cooperative CBR system for constructing a business management model
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
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An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented.