By Anschrift Dr. Cornelia Denz (auth.), Theo Tschudi (eds.)
ISBN-10: 3663122727
ISBN-13: 9783663122722
ISBN-10: 3663122743
ISBN-13: 9783663122746
In fresh years, there was a swift enlargement within the box of nonlinear optics as weIl as within the box of neural computing. brand new, not anyone could doubt that nonlinear optics is among the so much promising fields of understanding huge neural community types as a result of their inherent parallelism, using the rate of sunshine and their skill to technique two-dimensional info arrays with out providers or transformation bottlenecks. because of this why such a lot of of the attention-grabbing functions of nonlinear optics - associative stories, Hopfield networks and self-organized nets - are learned in an all optical method utilizing nonlinear optical processing components. either components attracting humans from a large choice of disciplines and judged via the proliferation of released papers, meetings, overseas collaborations and businesses, extra humans than ever prior to are actually in volved in study and purposes in those fields. those humans all convey a special historical past to the world, and one of many goals of this ebook is to supply a typical flooring from which new improvement can develop. one other goal is to provide an explanation for the fundamental innovations of neural computation as weIl as its nonlinear optical realizations to an audi ence. for that reason, the publication is ready the total box of optical neural community functions, masking all of the significant ways and their very important effects. specifically, it its an in troduction that develops the strategies and ideas from their easy fundamentals via their formula into strong experimental neural internet systems.
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Additional info for Optical Neural Networks
Sample text
3 Learning Difficulties OccasionaHy, the network may settle into a stable solution that does not provide the correct output - the energy function is in a local minimum as shown in fig. 11. This implies that in every direction in which the network could move, the energy is higher than at the current position. It may be that there is only a slight "hip" to cross before reaching an actual deeper minimum, but the network has no way of knowing this, because learning is accomplished by foHowing the energy function down in the steepest direction, until it reaches the bottom of a weH.
This approach is the one described extensively in the present chapter and has found great popularity with the development of the backpropagation algorithm, which permits the training of the intermediate hidden layers in a simple and successful way. However, we will see when discussing optical implementations of the 32 2 The Principles of Neural Networks x' ~ Y... 12: Outer product associative memory of the rth order. backpropagation algorithm, that difficulties may arise in realizing the partial derivatives of the thresholding function.
A diagram of a competitive learning network is shown in fig. 15. When an Ndimensional pattern of activity Cj is presented to the input layer, the input is weighted and interconnected by the M x N adaptive weight matrix Wij. J WijXj = Wi~ . 37) The M output neurons are grouped into K competitive patches, where the kth patch contains Mk competitive units, and M = L:f=t mk. The competitive patches can be stacked lexicographically so that the rows of the weight matrix with i E 1, ... , mt lead into the first patch that contains M} units, and the rows j E Mk + 1, ...
Optical Neural Networks by Anschrift Dr. Cornelia Denz (auth.), Theo Tschudi (eds.)
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