Adaptive Optical Neural Network Classifier Systems

Foor, Wesley E.; Getbehead, Mark A.; Rosetti, James B.

doi:10.21236/ada335118

Cited by 1 publication

(2 citation statements)

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“…An optical binary-input first layer was demonstrated in a previous project. 31 An electronic postprocessor that implements the second layer of the neural network would have to be fabricated to realize the system's full potential.…”

Section: Resultsmentioning

confidence: 99%

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Optical Neural Network Classifier Architectures

Getbehead¹,

Rosetti²,

Foor³

et al. 1998

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We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air Force Hostile Target Identification (HTI). This architecture utilizes a grayscale-input radial basis function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for the classifier by allowing it to handle 8 bit input data. We characterized a key component of this system, a variable phase retarder, and found that the phase uniformity changed less than 7% with applied voltage. An optical wavelet transform preprocessor is also discussed. The preprocessor produces a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network. The design uses a joint transform correlator (JTC) to provide cross correlations of multiple input images. We present experimental results for a JTC which used four input images generated with a spatial light modulator. We then propose using wavelet functions as input images to perform a multiwavelet feature extraction. The results from the retarder characterization and optical wavelet transform work were to be used in a software simulation of the neural network system to determine its feasibility. However, this work remains unfinished as this project was canceled due to budget cuts.

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Section: Resultsmentioning

confidence: 99%

“…More details of this binary-input system design and its application to handwritten digit recognition can be found in earlier publications 10 ' 30 and also in our report from a previous project. 31…”

Section: Binary Input Neural Network Classifiermentioning

confidence: 99%