In this report, a distributed neural network of coupled oscillators is applied to an industrial pattern recognition problem. The network stems from the study of the neurophysiology of the olfactory system. It is shown that the network serves as an associative memory, which possesses chaotic dynamics. The problem addressed is machine recognition of industrial screws, bolts, etc. in simulated real time in accordance with tolerated deviations from manufacturing specifications. After preprocessing, inputs are represented as 1 X 64 binary vectors. We show that our chaotic neural network can accomplish this pattern recognition task better than a standard Bayesian statistical method, a neural binary autoassociator, a three-layer feedforward network under back propagation learning, and our earlier olfactory bulb model that relies on a Hopf bifurcation from equilibrium to limit cycle. The existence of the chaotic dynamics provides the network with its capability to suppress noise and irrelevant information with respect to the recognition task. The collective effectiveness of the "cell-assemblies" and the threshold function of each individual channel enhance the quality of the network as an associative memory. The network classifies an uninterrupted sequence of objects at 200 ms of simulated real time for each object. It reliably distinguishes the unacceptable objects (i.e., 100% correct classification), which is a crucial requirement for this specific application. The effectiveness of the chaotic dynamics may depend on the broad spectrum of the oscillations, which may force classification by spatial rather than temporal characteristics of the operation. Further study of this biologically derived model is needed to determine whether its chaotic dynamics rather than other as yet unidentified attributes is responsible for the superior performance, and if so, how it contributes to that end.
