Alternating projection neural networks
نویسندگان
چکیده
منابع مشابه
Alternating Projection Neural Networks
We consider a class of neural networks whose performance can be analyzed and geometrically visualized in a signal space environment. Alternating projection neural networks (APNN’s) perform by alternatively projecting between two or more constraint sets. Criteria for desired and unique convergence are easily established. The network can he configured as either a content addressable memory or cla...
متن کاملHomogeneous and Layered Alternating Projection Neural Networks
We consider a class of neural networks whose performance can be analyzed and geometrically visualized in a signal space environment. Alternating projection neural networks (APNN's) perform by alternately projecting between two or more constraint sets. Criteria for desired and unique convergence are easily established. The network can be taught from a training set by viewing each library vector ...
متن کاملOptical-processor architectures for alternating-projection neural networks.
Optical-processor architectures for various forms of the alternating-projection neural network are considered. Required iteration is performed by passive optical feedback. No electronics or slow optics (e.g., phase conjugators) are used in the feedback path. The processor can be taught a new training vector by viewing it only once. If the desired outputs are trained to be either +/-1, then the ...
متن کاملFinite Precision Extended Alternating Projection Neural Network (FPEAP)
The paper studies finite precision Extended Alternating Projection Neural Network (FPEAP) and its related problems. An improved training method of FPEAP has been present after considering the finite precision influence on the training method of EAP. Then the mathematical relation among the factors influencing the association times has been studied. Finally simulation experiments have been desig...
متن کاملRelaxed Alternating Projection Methods
In this paper we deal with the von Neumann alternating projection method xk+1 = PAPBxk and with its generalization of the form xk+1 = PA(xk + k(PAPBxk xk)), where A;B are closed and convex subsets of a Hilbert space H and FixPAPB 6= ?. We do not suppose that A \ B 6= ?. We give su¢ cient conditions for the weak convergence of the sequence (xk) to FixPAPB in the general case and in the case A is...
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ژورنال
عنوان ژورنال: IEEE Transactions on Circuits and Systems
سال: 1989
ISSN: 0098-4094
DOI: 10.1109/31.90404