Artificial neuron with phase-encoded logic

Data processing: artificial intelligence – Neural network – Learning method

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Reexamination Certificate

active

07401058

ABSTRACT:
An artificial neuron includes an aggregator that combines a plurality of input signals. The value state of each of the input signals is encoded in a phase thereof. The artificial neuron also includes an actuator in communication with the aggregator. The actuator is configured to provide an output signal having a value state encoded in a phase thereof. The value state of the output signal may be selected on the basis of the value states of the input signals. The value state of each of the input signals and/or the output signal may be a logical state.

REFERENCES:
patent: 4027175 (1977-05-01), Hurst
patent: 5355435 (1994-10-01), DeYong et al.
patent: 5355436 (1994-10-01), Shin
patent: 5535309 (1996-07-01), Shin
patent: 5671336 (1997-09-01), Yoshida et al.
patent: 5696881 (1997-12-01), Wang
patent: 5781701 (1998-07-01), Wang
patent: 6151594 (2000-11-01), Wang
patent: 6269351 (2001-07-01), Black
patent: 6394952 (2002-05-01), Anderson et al.
patent: 6501294 (2002-12-01), Bernstein et al.
patent: 6708159 (2004-03-01), Kadri
patent: 6867051 (2005-03-01), Anderson et al.
patent: 6936476 (2005-08-01), Anderson et al.
patent: 7139740 (2006-11-01), Ayala
patent: 7164117 (2007-01-01), Breed et al.
patent: 7270970 (2007-09-01), Anderson et al.
patent: 7370019 (2008-05-01), Mattiussi et al.
Conditioned response training of robots using Adaptrode-based neural networks Mobus, G.E.; Fisher, P.S.; Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on vol. ii, Jul. 8-14, 1991 pp. 1002 vol. 2 Digital Object Identifier 10. 1109/IJCNN.1991.155678.
New skill learning paradigm using various kinds of neurons Tae-Dok Eom; Sung-Woo Kim; Changkyu Choi; Ju-Jang Lee; Intelligent Robots and Systems '96, IROS 96, Proceedings of the 1996 IEEE/RSJ International Conference on vol. 3, Nov. 4-8, 1996 pp. 1157-1164 vol. 3 Digital Object Identifier 10.1109/IROS.1996.568965.
Neural networks in computational science and engineering Cybenko, G.; Computational Science & Engineering, IEEE vol. 3, Issue 1, Spring 1996 pp. 36-42 Digital Object Indentifier 10.1109/99.486759.
Optical method for generalized Hebbian-rule in optical neural network Tsumura, N.; Fujii, Y.; Itoh, K.; Ichioka, Y.; Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on vol. 1, Oct. 25-29, 1993 pp. 833-836 vol. 1 Digital Object Identifier 10.1109/IJCNN.1993.714042.
Conditioned response training of robots using Adaptrode-based neural networks Mobus, G.E.; Fisher, P.S.; Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on vol. ii, Jul. 8-14, 1991 p. 1002 vol. 2 Digital Object Identifier 10.1109/IJCNN.1991.155678.
Nitta, T., “An extension of the back-propagation algorithm to complex numbers,” Neural Networks, 10 (8), 1391-1415, 1997.
Benvenuto, N., and Piazza, F., “On the complex backpropagation algorithm,” IEEE Transactions on Signal Processing, 40 (4), 967-969, 1992.
Leung, H., and Haykin, S., “The complex backpropagation algorithm,” IEEE Transactions on Signal Processing, 39 (9), 2101-2104, 1991.
Georgiou, G. M., and Koutsougeras, C., “Complex domain backpropagation,” IEEE Transactions on Circuits and Systems—II: Analog and Digital Signal Processing, 39 (5), 330-334, 1992.
Smith, M. R., and Hui, Y., “A data extrapolation algorithm using a complex domain neural network,” IEEE Transactions on Circuits and Systems—II: Analog and Digital Signal Processing, 44(2), 143-147, 1997.
Arena, P, Fortuna, G., Muscato, G., and Xibilia, M. G., “Multilayer Perceptrons to approximate quaternion valued functions,” Neural Networks, 10 (2), 335-342, 1997.
Hirose, A., “Dynamics of fully complex-valued neural networks,” Electronics Letters, 28 (16), 1492-1494, 1992.
Casasent, D., and Natarajan, S., “A classifier neural network with complex-valued weights and square-law nonlinearities,” Neural Networks, 8 (6), 989-998, 1995.
Weber, D. M. and Casasent, D. P., “The extended piecewise quadratic neural network,” Neural Networks, 11, 837-850, 1998.
Hirose, A., “Applications of complex-valued neural networks to coherent optical computing using phase-sensitive detection scheme,” Information Sciences, 2, 103-117, 1994.
Awwal, A. A. S. and Power G., “Object Tracking by an Opto-electronic Inner Product Complex Neural Network,” Optical Engineering, 32, 2782-2787, 1993.
Michel, H, E, and Awwal, A. A. S., “How to Train a Phase Only Filter”, in Advances in Optical Information Processing IX, Dennis R. Pape, Editor, Proceedings of SPIE vol. 4046, 2000.
Awwal, A. A. S., and Michel, H. E., “Enhancing the discrimination capability of phase only filter,” Asian Journal of Physics, vol. 8, No. 9, 2000.
Aizenberg, N. N., and Aizenberg, I. N., “Universal binary and multi-valued paradigm: Conception, learning, applications,” Lecture Notes in Computer Science, 1240, 463-472, 1997.
Igelnik, B., Tabib-Azar, M., and LeClair, S., “A net with Complex Weights,” IEEE Transactions on Neural Networks, 12(2), pp. 236-249, 2001.
Michel, H. E., and Kunjithapatham, S., “Processing Landsat TM data using complex-valued neural networks,” in Data Mining and Knowledge Discovery: Theory, Tools and Technology IV, Belur V. Dasarathy, Editor, Proceedings of SPIE (to be published) 2002.
Beiu, V., Quintana, J.M., Avedillo, M.J., “VLSI Implementations of Threshold Logic□A Comprehensive Survey”, IEEE Transactions on Neural Networks, 14(5).
Michel, H. E., Rancour, D., Iringentavida, S., “CMOS Implementation of Phase-Encoded Complex-Valued Artificial Neural Networks,” The 2004 International Conference on VLSI, Jun. 21-24, 2004.
Jain et al. “Artificial Neural Networks: A tutorial”. Computer, pp. 31-44, Mar. 1996.
Hopfield. “Pattern recognition computation using action potential timing for stimulus representation”. Nature 376:33-36, Jul. 1995.
Maass. “Lower Bounds for the Computational Power of Networks of Spiking Neurons”. Neural Computation 8:1-4, 1996.
McCulloch et al. “A logical calculus of the ideas immanent in nervous activity”. Bulletin of Mathematical Biophysics, 5:115-133, 1943.
Khan et al. “A parallel, distributed and associative approach for searching image patterns with holographic dynamics”. Journal of Visual Languages and Computing 8:303-331, 1997.
Elias et al. “Switched-Capacitor Neuromorphs with Wide-Range Variable Dynamics”. IEEE Transactions on Neural Networks 6(6):1542-1548, Nov. 1995.
Bayro-Corrochano. “Geometric Neural Computing”. IEEE Transactions on Neural Networks 12(5):968-986, Sep. 2001.
Pal et al. “Neurocomputing: Motivation, Models and Hybridization”. Computer, pp. 24-28, 1996.
Mortara et al. “A Communication Scheme for Analog VLSI Perceptive Systems”. IEEE Journal of Solid-State Circuits 30(6):660-669, Jun. 1995.
Khan. “Characteristics of Multidimensional Holographic Associative Memory in Retrieval with Dynamically Localized Attention”. IEEE Transactions on Neural Networks 9(3):389-406, May 1998.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Artificial neuron with phase-encoded logic does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Artificial neuron with phase-encoded logic, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Artificial neuron with phase-encoded logic will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3965048

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.