Method and apparatus for selecting an optimal number of trajectories from an available class of potential trajectories in a velocimetry application. In an exemplary method, a neural network is constructed wherein each neuron in the network represents a trajectory in the overall class. A binary output of each neuron indicates whether the trajectory represented by the neuron is to be selected. In the exemplary method, the neural network is initialized with a starting solution wherein the network is in an initially converged state. The network is then alternately excited and constrained so that it settles to additional converged states. During excitation, correction factors including a set-size maximizing term are applied to neuron input potentials. During constraint, the set-size maximizing terms are interrupted. Each time the network converges, the outputs of the neurons in the network are decoded to obtain a subset of trajectories which are to be selected. The excitation and constraint phases are then repeated in an attempt to obtain larger and larger subsets of trajectories to be selected. Once the excitation and constraint phases have been repeated a suitable number of times, the trajectories identified in the largest obtained subset of trajectories are selected.
Method and apparatus for using a neural network to extract an optimal number of data objects from an available class of data objects