Improving hopfield neural network performance by fuzzy logic-based coefficient tuning

Neurocomputing

Volumn 18, Issue 1-3, 1998, Pages 107-126

  Cavalieri, Salvatore ^a   Russo, Marco ^a  

^a UNIVERSITY OF CATANIA   (Italy)

Author keywords

Fuzzy logic; Hopfield neural network; NP hard optimization problem

Indexed keywords

COMBINATORIAL MATHEMATICS; COMPUTATIONAL COMPLEXITY; DATA STORAGE EQUIPMENT; FUZZY SETS; LEARNING ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; STORAGE ALLOCATION (COMPUTER);

FUZZY LOGIC BASED COEFFICIENT TUNING; HOPFIELD NEURAL NETWORKS;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; MODEL; PERFORMANCE; PRIORITY JOURNAL; PROBLEM SOLVING;

EID: 0031952323     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(97)00072-6     Document Type: Article

References (18)

1. [1] S.V.B. Aiyer, M. Niranjan, F. Fallside, A theoretical investigation into the performance of the Hopfield model, IEEE Trans Neural Networks 1 (2) (1990) 204-215.
2. [2] M. M. Ali, H.T. Nguyen, A neural network implementation of an input access scheme in a high-speed packet switch, Globecom (Dallas) (November 1989) 1192-1196.
3. [3] S. Cavalieri, Performance optimization of flexible manufacturing systems using artificial neural networks, in: Proc. 9th Internat. Conf. on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, IEA/AIE 96, Fukuoka, Japan, June 1996 (Gordon & Breach, Melbourne) 479-486.
4. [4] S. Cavalieri, Application of neural networks to factory automation performance optimization, in: Proc. of Conf. on Computational Engineering in Systems Applications, CESA 96, Lille, France, June 1996 (IMACS and IEEE) pp. 240-245.
5. [5] S. Cavalieri, A novel neural network model for the performance evaluation of flexible manufacturing systems, in: Proc. 22nd Annual Internat. Conf. on Industrial Applications, IECON96, Taipei, Taiwan, August 1996 (IEEE).
6. [6] S. Cavalieri, A solution to the end-effector position optimisation problem in robotics using neural networks, J. Neural Comput. Appl. 5 (1997) 45-57.
7. [7] S. Cavalieri, A, Di Stefano, O. Mirabella, Neural strategies to handle routing in computer networks, Internat. J. Neural Systems 4 (September 1993) 269-289.
8. [8] S. Cavalieri, A. Di Stefano, O. Mirabella, Optimal path determination in a graph by Hopfield neural network, Neural Networks 7 (1994) 397-404.
9. [9] S. Cavalieri, O. Mirabella, Neural networks for process scheduling in real-time communication systems, IEEE Trans. Neural Networks 7 (1996) 1272-1285.
10. [10] D.W. Gassen, J.D. Carothers, A Hopfield network for solving a complex scheduling problem in behavioral synthesis, IJCNN (1993) 1537-1540.
11. [11] J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. 79 (April 1882) 2554-2558.
12. [12] J.J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proc. Nat. Accad. Sci (May 1984) 3088-3092.
13. [13] J.J. Hopfield, D.W. Tank, Neural computations of decisions in optimization problems, Biol. Cyber. 52 (1986) 141-152.
14. [14] Y. Lin, G.A. Cunningham III, A new approach to fuzzy-neural system modeling, IEEE Trans. Fuzzy Systems 3 (May 1995) 190-198.
15. [15] Neural Ware, Neural Computing: A Technology Handbook for Professional 11/PLUS and Neural-Works Explorer, pp. 191-208. Neural Ware, Inc. Technical Publications Group, 202 Park West Drive, Pittsburg, PA 15275, USA: Neural Ware, 1995.
16. [16] M. Russo, Hybrid learning for fuzzy systems, in: Advances in Intelligent Systems (IOS Press, Amsterdam, Sept. 1997) pp. 440-445.
17. [17] H. Szu, Fast TSP algorithm based on binary neuron output and analog neuron input using the zero-diagonal interconnected matrix and necessary and sufficient constraints of the permutation matrix, in: Proc, 1st Internat. Conf. on Neural Networks, San Diego, CA, June 1987, pp. 259-266.
18. [18] Y. Takahashi, Mathematical improvement of the Hopfield model for TSP feasible solutions by synapse dynamical systems, Neurocomputing 15 (1997) 15-43.