Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653392 | Neurocomputing | 2005 | 31 Pages |
Abstract
A stochastic gradient is formulated based on deterministic gradient augmented with Cauchy simulated annealing capable to reach a global minimum with a convergence speed significantly faster when simulated annealing is used alone. In order to solve space-time variant inverse problems known as blind source separation, a novel Helmholtz free energy contrast function, H=E-T0S, with imposed thermodynamics constraint at a constant temperature T0 was introduced generalizing the Shannon maximum entropy S of the closed systems to the open systems having non-zero input-output energy exchange E. Here, only the input data vector was known while source vector and mixing matrix were unknown. A stochastic gradient was successfully applied to solve inverse space-variant imaging problems on a concurrent pixel-by-pixel basis with the unknown mixing matrix (imaging point spread function) varying from pixel to pixel.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Harold Szu, Ivica Kopriva,