Gheorghe-Marian Petrisor

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We analytically determine that the backward-error-propagation learning algorithm has a well-defined region of convergence in neural learning-parameter space for two classes of photorefractive-based optical neural-network architectures. The first class uses electric-field amplitude encoding of signals and weights in a fully coherent system, whereas the(More)
A gain and exposure schedule that theoretically eliminates the effect of photorefractive weight decay for the general class of outer-product neural-network learning algorithms (e.g., backpropagation, Widrow-Hoff, perceptron) is presented. This schedule compensates for photorefractive diffraction-efficiency decay by iteratively increasing the(More)
Robotics is a fascinating field of engineering that provides many opportunities for research. In addition, the evolution of technology in recent years has led to intelligent mobile robots. They can be sent in hard places for humans either because they are dangerous, either because they are difficult to access. The control of these robots, however, is a(More)
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