Neurofuzzy networks with nonlinear quantum learning

IEEE Transactions on Fuzzy Systems

Volumn 17, Issue 3, 2009, Pages 698-710

  Panella, Massimo ^a   Martinelli, Giuseppe ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Accuracy; Approximation algorithms; Array signal processing; Artificial neural networks; Atmospheric measurements; Boolean functions; Capacitors; Cavity resonators; Clustering algorithms; CMOS integrated circuits; CMOS technology; Cognition; Complexity theory; Computational efficiency; Computational intelligence; Computational modeling; Computer architecture; Computers; Correlation; Data mining; Digital circuits; Digital signal processors; Electron traps; Exhaustive search; Field programmable gate arrays; Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Genetics; Granular superconductors; Inference mechanisms; Input variables; Knowledge engineering; Linear programming; Linearity; Logic circuits; Logic gates; Mathematics; Mechanical factors; Mechanical variables measurement; Neurofuzzy system; Nonlinear quantum processing; Nuclear magnetic resonance; Optimization; Parallel processing; Particle measurements; Pattern matching; Performance evaluation; Periodic structures; Photonics; Physics; Programmable logic arrays; Quantum computing; Quantum entanglement; Quantum mechanics; Quantum neurofuzzy network; Reliability; RLC circuits; Rotation measurement; Search methods; Search problems; Signal processing algorithms; Training; Vectors

Indexed keywords

ACCURACY; ARRAY SIGNAL PROCESSING; ARTIFICIAL NEURAL NETWORKS; ATMOSPHERIC MEASUREMENTS; CMOS TECHNOLOGY; COGNITION; COMPLEXITY THEORY; COMPUTATIONAL MODELING; CORRELATION; FUZZY REASONING; INFERENCE MECHANISMS; INPUT VARIABLES; MATHEMATICS; PARALLEL PROCESSING; PARTICLE MEASUREMENTS; PERFORMANCE EVALUATION; PROGRAMMABLE LOGIC ARRAYS; QUANTUM COMPUTING; QUANTUM MECHANICS; RLC CIRCUITS; ROTATION MEASUREMENT; SEARCH METHODS; SEARCH PROBLEMS; SIGNAL PROCESSING ALGORITHMS; TRAINING;

ATMOSPHERIC RADIATION; BACKPROPAGATION; BOOLEAN FUNCTIONS; CAVITY RESONATORS; CIRCUIT RESONANCE; CLUSTERING ALGORITHMS; CMOS INTEGRATED CIRCUITS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COMPUTER ARCHITECTURE; COMPUTER CIRCUITS; COMPUTER NETWORKS; COMPUTER SYSTEMS PROGRAMMING; DIGITAL INTEGRATED CIRCUITS; DYNAMIC PROGRAMMING; EDUCATION; ELECTRON TRAPS; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); FUZZY INFERENCE; FUZZY LOGIC; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; INFERENCE ENGINES; INFORMATION MANAGEMENT; INTEGRATED CIRCUITS; KNOWLEDGE ENGINEERING; LEARNING ALGORITHMS; LINEAR PROGRAMMING; LINEARIZATION; LOGIC GATES; LUMPED PARAMETER NETWORKS; MECHANICAL VARIABLES MEASUREMENT; MECHANICS; MECHANISMS; MEMBERSHIP FUNCTIONS; MICROWAVE DEVICES; MINING; MOS CAPACITORS; NETWORK PERFORMANCE; NUCLEAR MAGNETIC RESONANCE; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; OPTIMIZATION; PARALLEL ALGORITHMS; PARALLEL PROCESSING SYSTEMS; PERIODIC STRUCTURES; PROGRAMMABLE LOGIC CONTROLLERS; QUANTUM COMPUTERS; QUANTUM ENTANGLEMENT; QUANTUM OPTICS; RELIABILITY THEORY; ROTATION; SIGNAL PROCESSING; SWITCHING CIRCUITS; SYSTEMS ENGINEERING;

APPROXIMATION ALGORITHMS;

EID: 67149088591     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2008.928603     Document Type: Article

References (32)

1. Los Alamos preprint server. (2006). [Online]. Available: http:// xxx.lanl.gov/archive/quant-ph
2. S. C. Kak, "Quantum neural computing," Adv. Imag. Electron Phys. vol. 94, pp. 259-314, 1995.
3. G. Purushothaman and N. B. Karayiannis, "Quantum neural networks (QNNs): Inherently fuzzy feedforward neural networks," IEEE Trans. Neural Netw., vol. 8, no. 3, pp. 679-693, May 1997.
4. A. Narayanan and T. Menneer, "Quantum artificial neural network architectures and components," Inf. Sci., vol. 128, pp. 231-255, 2000.
5. A. A. Ezhov and D. Ventura, "Quantum neural networks," in Future Directions for Intelligent Systems and Information Sciences, N. Kasabov, Ed. Berlin, Germany: Springer-Verlag, 2000, pp. 213-234.
6. S. Gupta and R. K. P. Zia, "Quantum neural networks," J. Comput. Syst. Sci., vol. 63, pp. 355-383, 2001.
7. M. A. Perkowski, "Multiple-valued quantum circuits and research challenges for logic design and computational intelligence communities," IEEE Connections, vol. 3, no. 4, pp. 6-12, Nov. 2005.
8. M. Panella and G. Martinelli "Binary neuro-fuzzy classifiers trained by nonlinear quantum circuits," in Applications of Fuzzy Sets Theory, Lecture Notes in Artificial Intelligence, vol. 4578, F. Masulli, S. Mitra, and G. Pasi, Eds. Berlin, Germany: Springer-Verlag, 2007, pp. 237-244.
9. A. Malossini, E. Blanzieri, and T. Calarco, "Quantum genetic optimization," IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 231-241, Apr. 2008.
10. G. J. Iafrate and M. A. Scorsio, "Application of quantum-based devices: Trends and challenges," IEEE Trans. Electron Devices, vol. 43, no. 10, pp. 1621-1625, Oct. 1996.
11. J. I. Cirac and P. Zoller, "Quantum computations with cold trapped ions," Phys. Rev. Lett., vol. 74, pp. 4091-4094, 1995.
12. D. Stick, W. Hensinger, S. Olmschenk, M. Madsen, K. Schwab, and C. Monroe, "Ion trap in a semiconductor chip," Nat. Phys., vol. 2, pp. 36-39, Jan 2006.
13. N. A. Gershenfeld and I. L. Chuang, "Bulk spin resonance quantum computing," Sci., vol. 275, pp. 350-356, 1997.
14. A. Papageorgiou and J. F. Traub. (2006, Apr.) Qbit complexity of continuos problems. ArXiv:qUant-Hh/0512082. [Online]. Available: http:// www.lanl.gov/archive/quant-ph
15. M. J. Patyra, "Guest editorial: Fuzzy logic hardware implementations," IEEE Trans. Fuzzy Syst., vol. 4, no. 4, pp. 401-402, Nov. 1996.
16. F. Salam and T. Yamakawa, Eds., "Special issue on microelectronic hardware implementation of soft computing: Neural and fuzzy networks with learning," (Comput. Electron. Eng.), vol. 25, no. 5, 1999.
17. I. Baturone and S. Sanchez-Solano, "Microelectronic design of universal fuzzy controllers," Mathware Soft Comput., vol. 8, pp. 303-319, 2001.
18. D. Anguita, I. Baturone, and J. Miller, Eds. "Special Issue on Hardware Implementations of Soft Computing Techniques," (Appl. Soft Comput.), vol. 4, 2004.
19. K. Basterretxea, J. M. Tarela, I. del Campo, and G. Bosque, "An experimental study on nonlinear function computation for neural/fuzzy hardware design," IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 266-283, Jan. 2007.
20. P. Echevarria, M. V. Martinez, J. Echanobe, I. del Campo, and J.M. Tarela, "Digital hardware implementation of high dimensional fuzzy systems," in Applications of Fuzzy Sets Theory, Lecture Notes in Artificial Intelligence, vol. 4578, F. Masulli, S. Mitra, and G. Pasi, Eds. Berlin, Germany: Springer-Verlag, 2007, pp. 245-252.
21. E. Rieffel and W. Polak, "An introduction to quantum computing for nonphysicists," ACM Comput. Surv., vol. 32, pp. 300-335, 2000.
22. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Upper Saddle River, NJ: Prentice-Hall, 1997.
23. M. Brown and C. Harris, Neurofuzzy Adaptive Modeling and Control. (International Series in Systems and Control Engineering). Upper Saddle River, NJ: Prentice-Hall, 1995.
24. F. M. Frattale Mascioli and G. Martinelli, "A constructive approach to neurofuzzy networks," Signal Process., vol. 64, pp. 347-358, 1998.
25. S. Haykin, Neural Networks (A Comprehensive Foundation), 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
26. M. Panella and A. S. Gallo, "An input-output clustering approach to the synthesis of ANFIS networks," IEEE Trans. Fuzzy Syst., vol. 13, no. 1, pp. 69-81, Feb. 2005.
27. D. Deutsch, "Quantum theory, the church-turing principle, and the universal quantum computer," Proc. Roy. Soc. Ser. A, London, U.K., vol. 400, pp. 97-117, 1985.
28. V. Vedral, A. Barenco, and A. Ekert, "Quantum networks for elementary arithmetic operations," Phys. Rev. A, Gen. Phy., vol. 54, pp. 147-153, 1996.
29. L. K. Grover, "A fast quantum mechanical algorithm for database search," in Proc. 28th Annu. ACM Symp. Theory Comput., Philadelphia, PA, May 1996, pp. 212-219.
30. D. Abrams and S. Lloyd, "Nonlinear quantum mechanics implies polynomial-time solution for NP-complete and #P problems," Phys. Rev. Lett., vol. 81, pp. 3992-3995, 1998.
31. M. Czachor. (1998, Feb.). Notes on nonlinear quantum algorithms. ArXiv:qUant-ph/9802051v2., [Online]H Available: http://www.lanl.gov/ archive/quant-ph
32. M. Czachor. (1998, Mar.). Local modification of the Abrams-Lloyd nonlinear algorithm. ArXiv:qUant-ph/9803019v1. [Online]. Available: http://www.lanl.gov/archive/quant-ph