Associative memories based on fuzzy mathematical morphology and an application in prediction

Fuzzy associative memories belong to the class of fuzzy neural networks that employ fuzzy operators such as fuzzy conjunctions, disjunctions, and implications in order to store associations of fuzzy patterns. Fuzzy associative memories are generally used to implement fuzzy rule-based systems. Applications of FAMs include backing up a truck and trailer, target tracking, human-machine interfaces, robot control, and voice cell control in ATM networks [3]. Recently, we observed that many well-known FAM models perform elementary operations of fuzzy mathematical morphology at every node [5, 8]. Therefore, many FAM models can be viewed as fuzzy morphological neural networks or more precisely as fuzzy morphological associative memories (FMAMs). Fuzzy morphological neural networks and FMAMs, in particular, involve concepts from the areas of mathematical morphology, fuzzy set theory, and artificial neural networks. We intend to provide a detailed analysis of FMAMs in the near future. In particular, we plan to explore the mathematical morphology aspects of FMAMs by developping a more general recording strategy for FMAMs that is based on the notion of adjunction.