Improved dynamic neural filtering technique by Widrow-recurrent learning algorithm

Abdullah Bal

doi:10.1117/12.604038

28 March 2005 Improved dynamic neural filtering technique by Widrow-recurrent learning algorithm

Abdullah Bal

Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.604038
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Neural network based image processing algorithms present numerous advantages due to their supervised adjustable weight and bias coefficients. Among various neural network architectures, dynamic neural networks, Hopfield and Cellular neural networks have been found inherently suitable for filtering applications. These kind of neural networks present two important features; supervised learnable and optimization properties. Using these properties, dynamic neural filtering technique has been developed based on Hopfield neural networks. The filtering structure involves adjustable a filter mask and 2D convolution operation instead of weight matrix operations. To improve the supervised training properties, Widrow-recurrent learning algorithm has been proposed in this paper. Since the proposed learning algorithm requires less computation, consumption time in the training stage has been decreased considerably compared to previous reported supervised techniques for dynamic neural filtering.

Citation Download Citation

Abdullah Bal "Improved dynamic neural filtering technique by Widrow-recurrent learning algorithm", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); https://doi.org/10.1117/12.604038

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KEYWORDS

Neural networks

Image filtering

Feature extraction

Convolution

Edge detection

Evolutionary algorithms

Image processing

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