Boots – shoes – and leggings
Patent
1989-06-12
1991-12-31
MacDonald, Allen R.
Boots, shoes, and leggings
364736, 395 50, 395 51, 395 61, 395 3, 395900, G06F 1518
Patent
active
050776770
ABSTRACT:
The present system performs linear transformations on input probabilities and produces outputs which indicate the likelihood of one or more events. The transformation performed is a product of linear transforms such as P.sub.o =[A.sub.j P.sub.j +B.sub.j ].multidot.[A.sub.k P.sub.k +B.sub.k ] where P.sub.j and P.sub.k are input probabilities, P.sub.o is an output event probability and A.sub.j, B.sub.j, A.sub.k and B.sub.k are transformation constants. The system includes a basic processing unit or computational unit which performs a probabilistic gate operation to convert two input probability signals into one output probability signal where the output probability is equal to the product of linear transformations of the input probabilities. By appropriate selection of transformation constants logical and probabilistic gates performing the functions of AND, NAND, OR, NOR, XOR, NOT, IMPLIES and NOT IMPLIES can be created. The basic unit can include three multipliers and two adders if a discrete component hardwired version is needed for speed or a single multiplier/adder, associated storage and multiplex circuits can be used to accomplish the functions of the hardwired version for economy. This basic unit can also be provided as a software implementation, can be implemented as a hardwired decision tree element implementation or implemented as a universal probabilistic processor and provided with a bus communication structure to create expert systems or neural networks suitable for specific tasks. The basic units can be combined to produce a virtual basic building block which has more virtual processors than physical processors to improve processor utilization. The building blocks can be combined into an array to produce either a high speed expert system or a high speed neural network.
REFERENCES:
patent: 3259735 (1966-07-01), Senstad
patent: 3757261 (1973-09-01), Sather
patent: 3803390 (1974-04-01), Schaepman
patent: 4593367 (1986-06-01), Slack et al.
patent: 4594678 (1986-06-01), Unlenhoff
patent: 4599692 (1986-07-01), Tan et al.
patent: 4611305 (1986-09-01), Iwase
patent: 4620188 (1986-10-01), Sengchanh
patent: 4620286 (1986-10-01), Smith et al.
patent: 4641356 (1987-02-01), Sternberg
patent: 4694418 (1987-09-01), Ueno et al.
patent: 4875184 (1989-10-01), Yamakawa
patent: 4945506 (1990-07-01), Baji et al.
A Probabilistic Causal Model for Diagnostic Problem Solving--Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference; Peng et al., IEEE Trans. on Systems, Man, and Cybernetics; vol. SMC-17, No. 2; Mar./Apr. 1987; pp. 146-162.
J. R. Quinlan (1987) "Inductive Knowledge Acquisition: a Case Study" in Applications of Expert Systems J. R. Quinlan, ed. pp. 157-173. Addison-Wesley, NY.
C. Johnson (1988) "In Search of the Silicon Brain" in Electrical Engineering Times Jun. 3, pp. 65.
Journal of the Association for Computing Machinery, `"Direct Search` solution of Numerical and Statistical Problems" vol. 2, pp. 212-229.
Richard O. Duda, Peter E. Hart and Nils J. Nillson, "Subjective Bayesian methods for Rule-Based Inference Systems", vol. 2, pp. 192-199, 212-229.
Dennis V. Lindley, Statistical Science, 1987, vol. 2, No. 1, 3-44 "The Probability Approach to the Treatment of Uncertainty in Artificial Intelligence and Expert Systems" pp. 17-24.
Implementing Neural Nets with Programmable Logic; J. J. Vidal; IEEE Transactions on Acoustics, Speech, and Signal Processing; vol. 36, No. 7, Jul. 1987; pp. 1180-1190.
Modern Dictionary of Electronics; Rudolf F. Graf; Howard W. Sams & Co., Inc; 1977, p. 473.
An Introduction to Computing with Neural Nets; IEEE ASSP Magazine Apr. 1987; Lippmann; pp. 4-21.
K. Fukushima, S. Miyake and T. Ito, "Neocognitron: a Neural Network Model for a Mechanism of Visual Pattern Recognition" IEEE Transactions on Systems, Man, and Cybernetics SMC-13:826-834.
Robert Hecht-Nielsen, Komogorov's Mapping Neural Network Existence Theorem, pp. 11-13.
W. S. McCulloch & W. H. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, pp. 19-39.
E. A. Feigenbaum, The Art of Artificial Intelligence, pp. 1014-1029.
Robert Hecht-Nielsen, Neurocomputing: picking the human brain, IEEE Spectrum, Mar. 1988, pp. 36, 41.
D. E. Rumelhart, G. E. Hinton & R. J. Williams, Learning Internal Representations by Error Propagation, pp. 319-362.
Robert Hecht-Nielsen, Neurocomputer Applications, pp. 445-453.
J. R. Ball, R. C. Bollinger, T. A. Jeeves, R. C. McReynolds, D. H. Shaffer, On the Use of the Solomon Parallel-Processing Computer, pp. 137-146.
"The Acquistion Problem is the Most Critical `Bottleneck` Problem" E. A. Feigenbaum (1981) Expert Systems in the 1980s in State of the art report on machine intelligence A. Bond, ed. Pergamon-Infotech, Maindenhead.
Anderson Arthur A.
Jeeves Terry A.
Murphy John H.
MacDonald Allen R.
Sutcliff W. G.
Westinghouse Electric Corp.
LandOfFree
Probabilistic inference gate does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Probabilistic inference gate, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Probabilistic inference gate will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-1514517