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Abstract

The present invention provides a system for learning from and responding to regularly arriving information at once by quickly combining prior information with concurrent trial information to produce useful learned information. At the beginning of each time trial a vector of measurement values and a vector of measurement plausibility values are supplied to the system, and a learning weight is either supplied to or generated by the system. The system then performs the following operations during each time trial: converting the measurement values to feature values; converting the measurement plausibility values to feature viability values; using each viability value to determine missing value status of each feature value; using non-missing feature values to update parameter learning; imputing each missing feature value from non-missing feature values and/or prior learning; converting imputed feature values to output imputed measurement values; and supplying a variety of feature value and feature function monitoring and interpretation statistics. A parallel embodiment of the system performs all such operations concurrently, through the coordinated use of parallel feature processors and a joint access memory, which contains connection weights and provision for connecting feature processors pairwise. The parallel version also performs feature function monitoring, interpretation and refinement operations promptly and in concert with concurrent operation, through the use of a parallel subsystem that receives interpretable concurrent connection weights from a parallel port. A nonparallel embodiment of the system uses a single processor to perform the above operations however, more slowly than the parallel embodiment, yet faster than available alternatives.

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First Claim