Timed neural nets for moving target recognition

We propose a timed neural net (TNN) approach to the problem of recognition of moving targets. We consider a synchronous timed Petri net (TPN) as a model for this timed neural net. In a TPN the transitions are enabled and fired by using a 'time' token. A group of place nodes and their corresponding transition nodes model a neuron in a TNN. In order to classify the type of motion that a moving target is executing, we look upon an image sequence as a single image evolving in time. The reachability set, R(t) at any instant of time represents a snapshot of the weight matrix of a static neural net recognizing the target. The motion classification is achieved by analyzing R(t). An example illustrating the approach is constructed.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.