1 August 1994 Neural network parameter effects on object classification and wavelength selection
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Optical Engineering, 33(8), (1994). doi:10.1117/12.173579
Abstract
A back propagative neural network (NN) was used to select visible spectrum (400 to 700 nm) wavelengths and classify damaged and undamaged peanut kernels. Results showed kernel classifications were best, network errors were minimized, and speed of convergence was greatest when the NN was set up with 20 or more hidden nodes, a momentum of 0.45 or less, and using about 1,000,000 learning events. Reflectance data in the 620 to 700-nm range were most influential in classifying kernels followed by relative reflectance in the 400 to 480-nm range. The learning rate did not affect NN performance, but higher learning rates converged more quickly. The most accurate classification performance occurred when the NN had 40 hidden nodes and a momentum of 0.45. These settings resulted in correct classification of 87.8% of all kernels. When compared to statistical means of classifying kernels using data from specific wavelengths or data from a colorimeter, the NN correctly classified about 5% and 13% more kernels, respectively.
Floyd E. Dowell, "Neural network parameter effects on object classification and wavelength selection," Optical Engineering 33(8), (1 August 1994). http://dx.doi.org/10.1117/12.173579
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KEYWORDS
Neural networks

Inspection

Reflectivity

Error analysis

Optical inspection

Spectrophotometry

Visualization

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