On the convergence and parameter relation of discrete-time continuous-state hopfield networks with self-interaction neurons

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Volumn E84-A, Issue 12, 2001, Pages 3162-3173

  Feng, Gang ^a   Douligeris, Christos ^b  

^a UNIVERSITY OF MIAMI   (United States)

^b UNIVERSITY OF PIRAEUS   (Greece)

Author keywords

Discrete time convergence; Hopfield neural networks; Parameter relationship; Self interaction neurons

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CONVERGENCE OF NUMERICAL METHODS; INTEGER PROGRAMMING; LAGRANGE MULTIPLIERS; LINEAR PROGRAMMING; MATRIX ALGEBRA; NONLINEAR PROGRAMMING; PARAMETER ESTIMATION; STATISTICAL METHODS; THEOREM PROVING;

DISCRETE-TIME CONTINUOUS-STATE HOPFIELD NETWORK; DISCRETE-TIME CONVERGENCE THEOREM; ENERGY FUNCTION; GUIDED TRIAL-AND-ERROR TECHNIQUE; HOPFIELD NEURAL NETWORK; SELF-INTERACTION NEURON;

NEURAL NETWORKS;

EID: 0035670318     PISSN: 09168508     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Neurons with graded response have collective computational properties like those of two-state neurons
2. Neural computation of decisions in optimization problems
3. Finding knight's tours on an M × N chessboard with O(MN) hysteresis McCulloch-Pitts neurons
4. Discrete-time convergence theory and updating rules for neural networks with energy functions
5. Problem solving with optimization networks
6. A theoretical investigation into the performance of the Hopfield model
7. Global convergence and suppression of spurious states of the Hopfield neural networks
8. The stability of the solution in Hopfield neural network
9. Theoretical characterizations of possibilities and impossibilities of Hopfield neural networks in solving combinatorial optimization problems
10. Theoretical comparison of mappings of combinatorial optimization problems to Hopfield networks (extended abstract)
11. "Optimal" Hopfield network for combinatorial optimization with linear cost function
12. The Hungarian method for the assignment problem
13. Using Hopfield networks to solve traveling salesman problems based on stable state analysis technique
14. Competitive neural architecture for hardware solution to the assignment problem
15. Primal and dual assignment networks
16. Inhibitory grids and the assignment problem
17. Statistical physics algorithms that converge
18. Hysteretic Hopfield neural network
19. Fast TSP algorithm based on binary neuron output and analog neuron input using the zero diagonal interconnect matrix and necessary and sufficient constraints of the permutation matrix
20. Effective neural algorithms for the traveling salesman problem
21. A neural network for solving the traveling salesman problem
22. Artificial neural networks for four-coloring map problems and K-colorability problems
23. Solving combinatorial optimization problem by chaotic neural networks
24. A modified Hopfield model for solving the N-queen problem
25. Dual-mode dynamics neural networks for non-attacking N-queen problem
26. On the self-feedback controlled chaotic neural networks and its application to N-queen problem