parvis@pyr.gatech.EDU (FULLNAME) (03/29/89)
I am looking for approaches using neural nets for forecasting based on history data. Currently I'm using various sequential models similar to Jordan's network. I am testing my models on a sequence of notes in a melody. The network should be able to reproduce the whole sequence based on initial input. Jordan's network seems to have problems with melodies where one note could have many successor notes. The more different successor values a specific value in the sequence has, the longer it takes for the network to learn. What other possibilities should I explore to represent context, time, in general: sequential dependencies? I appreciate any suggestions and comments. Parvis Avini parvis@gitpyr.gatech.edu