Boots – shoes – and leggings
Patent
1992-11-25
1994-07-12
Nguyen, Long T.
Boots, shoes, and leggings
G06G 718
Patent
active
053294785
ABSTRACT:
A circuit and method for estimating gradients of a target function using noise injection and correlation is provided. In one embodiment, an input signal is combined with an input noise signal and the combined signal is input to a circuit which computes the output of the target function. An amplified noise signal and output signal of the target function are input to a multiplier which performs a correlation of the inputs. The output of the multiplier is processed by a low-pass filter which generates the gradient. The circuit and method can be expanded to N-dimensions. Furthermore, in a alternate embodiment, a differentiator is coupled between the multiplier and amplifier and the multiplier and the output of the target function to differentiate the two signals prior to input to the multiplier. In other embodiments, the circuit may be used to compute gradient-like signals, wherein each component of the gradient is individually scaled by a different value. The output of the circuit can then be used in other descent algorithms. In addition, varying the scale of the noise signal over a time schedule, an annealing style of optimization can be implemented. This prevents the gradient process from stopping at local minima while descending to the global minimum of the function.
REFERENCES:
patent: 5099156 (1992-03-01), Delbruck et al.
patent: 5168459 (1992-12-01), Hiller
Alspector, J., J. W. Gannett, S. Haber, M. B. Parker, and R. Chu, "A VLSI-Efficient Technique for Generating Multiple Uncorrelated Noise Sources and Its Application to Stochastic Neural Networks," IEEE Transactions on Circuits and System, vol. 38, No. 1, pp. 109-123, Jan. 1991.
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Delbruck, Tobi, "`Bump` Circuits for Computing Similarity and Dissimilarity of Analog Voltages," Caltech Computation and Neural Systems Memo No. 10, May 23, 1991.
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Kirk, et al., "Constrained Optimization Applied to the Parameter Setting Problem for Analog Circuits," IEEE Neural Information Processing Systems 1991 (NIPS91), Morgan Kaufman, San Diego, 1991.
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Anderson Brooke P.
Barr Alan H.
Fleischer Kurt
Kerns Douglas A.
Kirk David B.
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