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Neural network for performing a relaxation process

  • US 5,274,744 A
  • Filed: 01/21/1992
  • Issued: 12/28/1993
  • Est. Priority Date: 01/21/1992
  • Status: Expired due to Term
First Claim
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1. A neural network for implementing a probabilistic relaxation process comprisinga plurality of interconnected processing nodes, each node generating an output signal representing the time evolution of a probability that a particular object in a set of objects is a member of a particular class in a set of classes,said nodes being interconnected by weighted connection paths such that each node receives a time dependent input signal including a sum of weighted input signals of a plurality of said nodes and an externally generated input signal, the weighting of a connection path connecting two particular nodes being dependent on a compatibility that the corresponding objects of the nodes will be in the corresponding classes of the nodes.each of said processing nodes having a transfer function characteristic so that its output signal is a monotonic non-linear function of its input signal,wherein when the output signal of each node is first set to an initial estimated value of the probability that a particular object in a set of objects is a member of a particular class in a set of classes, said output signal of each node evolves over time due to the interconnection between said nodes to a constant final value indicative of whether or not a particular object is in a particular class,wherein said neural network has an energy function ##EQU18## wherein A and B are positive numbersc(i,j;

  • h,k) is the compatibility that an object i is in a class j and an object h is in a class k.Vu is the output signal of a node with index ijVhk is the output signal of a node with index hkn is the number of objects.

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