Data processing: artificial intelligence – Neural network – Structure
Reexamination Certificate
2008-07-08
2008-07-08
Hirl, Joseph P (Department: 2129)
Data processing: artificial intelligence
Neural network
Structure
C706S027000, C706S013000
Reexamination Certificate
active
10791249
ABSTRACT:
An Effector machine is a new kind of computing machine. When implemented in hardware, the Effector machine can execute multiple instructions simultaneously because every one of its computing elements is active. This greatly enhances the computing speed. By executing a meta program whose instructions change the connections in a dynamic Effector machine, the Effector machine can perform tasks that digital computers are unable to compute.
REFERENCES:
patent: 3950733 (1976-04-01), Cooper et al.
patent: 4660166 (1987-04-01), Hopfield
patent: 4962342 (1990-10-01), Mead
patent: 5155802 (1992-10-01), Mueller
patent: 5325464 (1994-06-01), Pechanek
patent: 5369731 (1994-11-01), Masumoto
patent: 5422983 (1995-06-01), Castelaz
patent: 5524175 (1996-06-01), Sato
patent: 5524176 (1996-06-01), Narita
patent: 5619618 (1997-04-01), Bigus
patent: 5832466 (1998-11-01), Feldgajer
patent: 6049793 (2000-04-01), Tomita
patent: 6256619 (2001-07-01), Grichnik
patent: 6332137 (2001-12-01), Hori
patent: 6470261 (2002-10-01), Ng et al.
patent: 6601053 (2003-07-01), Schaffer et al.
patent: 6751601 (2004-06-01), Zegers
patent: 6754645 (2004-06-01), Shi
patent: 2003/0212645 (2003-11-01), Schaffer et al.
patent: 2004/0128004 (2004-07-01), Adams
patent: 2006/0277533 (2006-12-01), Fiske
patent: 2007/0079108 (2007-04-01), Fiske
patent: 2007/0288668 (2007-12-01), Fiske
patent: WO/2007/008519 (2007-01-01), None
Lindgren et. at. “Regular Language Inference Using Evolving Neural Networks” IEEE 1992.
Elias,J.G. “Genetic Generation of Connection Patterns for a Dynamic Artificial Neural Network” IEEE 1992.
Dasgupta et. al. “Designing Application-Specific Neural Networks using the Structured Genetic Algorithm” IEEE 1992.
Schaffer et. at. “Combinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art” IEEE 1992.
de Garis,H. et al. “The CAM-Brain Machine (CBM): an FPGA-based hardware tool that evolves a 1000 neuron-net circuit module in seconds and updates a 75 million neuron artificial brain for real-time robot control” Neurocomputing. 2002.
Siegelmann,H.T. et al. “Neural Networks and Analog Computation: Beyond the Turing Limit” pp. 29-33,153-164. Birkhäuser. 1999.
Azam,F. “Biologically Inspired Modular Neural Networks” PhD Dissertation. Virginia Tech. May 2000.
Mead,C. “Analog VLSI and Neural Systems” , 1989, pp. 36-39,37-81,208-209,260-263, Addison-Wesley, United States.
Marian,I.D. “A biologically inspired model of motor control of direction” MS Thesis. University College Dublin, Ireland. Oct. 2002.
Atsumi,M. “Artificial Neural Development for Pulsed Neural Network Design—A Simulation Experiment on Animat's Cognitive Map Genesis” IEEE. 2000.
Hérault,L. “Optimization by pulsed recursive neural networks” IEEE. 1995.
Gallagher,J.C. et al. “Continuous Time Recurrent Neural Networks: A Paradigm for Evolvable Analog Controller Circuits” IEEE. 2000.
Floreano,D. et al. “Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots” 2001.
G.E.P. Box, Evolutionary operation: A method for increasing industrial production, Applied Statistics, 1957, pp. 81-101 vol. 6, No. 2, Royal Statistical Society, England.
H.J. Bremerman, Optimization Through Evolution and Recombination, Self-Organizing Systems, 1962, pp. 93-106, Spartan Books, Washington D.C.
Carver Mead, Analog VLSI and Neural Systems, 1989, pp. 36-39, 67-81, 208-209, 260-263, Addison-Wesley, United States.
Wilrid Rall, Theoretical Significance of Dendritic Trees for Neuronal Input-Output Relations, The Theoretical Foundation of Dendritic Function, 1964, pp. 122-145, MIT Press.
Alan Turing, on Computable Numbers with an Application to the Entscheidungs problem, 1936, Proceedings London Mathematical Society, pp. 230-265, vol. 42, pp. 544-546, vol. 43, England.
George J. Friedman. Digital Simulation of an Evolutionary Process. 1959. pp. 171-184. vol. 4 of General Systems Yearbook.
W. W. Bledsoe. The use of biological concepts in the analytical study of systems. 1961. ORSA-TIMS National Meeting. San Francisco. pp. 1-25.
Buss Benjamin
Fiske Software LLC
Hirl Joseph P
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