A physics based neural network (PBNN) for detecting trends in a series of data inputs comprising a neural filter comprising a plurality of nodes for receiving the series of data inputs and outputting a plurality of averaged outputs, at least one standard deviation node for receiving one of the plurality of averaged outputs and the series of data inputs to produce at least one standard deviation output, wherein at least one of the average outputs is a delayed average output and at least one of the standard deviation outputs is a delayed standard deviation output, and a neural detector comprising a plurality of neural detector nodes receiving the plurality of averaged outputs and the delayed average output and outputting a neural detector output, a neural level change node receiving the plurality of averaged outputs and outputting a neural level change estimate output, a neural confidence node receiving a counter input, the delayed standard deviation output, and the neural level change estimate output and outputting a neural assessment output, and a heuristic detector comprising a plurality of detector nodes receiving the averaged inputs, the delayed average input, the series of data inputs, and the delayed standard deviation output and outputting a confidence level output, wherein the neural assessment output and the confidence level output are combined to determine an event in the series of data inputs.
Physics based neural network trend detector