Determination of weights for relaxation recurrent neural networks

Neurocomputing

Volumn 34, Issue 1-4, 2000, Pages 145-168

  Serpen, Gürsel ^a   Livingston, David L ^a  

^a The University of Toledo   (United States)

Author keywords

Dynamic system stability; Hopfield networks; Optimization; State space relaxation; Weight parameters

Indexed keywords

ALGORITHMS; BOUNDARY CONDITIONS; COMPUTER SIMULATION; CONSTRAINT THEORY; CONVERGENCE OF NUMERICAL METHODS; PARAMETER ESTIMATION; STATE SPACE METHODS; SYSTEM STABILITY; TRAVELING SALESMAN PROBLEM;

DYNAMIC SYSTEM STABILITY; HOPFIELD NETWORKS; STATE SPACE RELAXATION; WEIGHT PARAMETERS;

RECURRENT NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; MATHEMATICAL ANALYSIS; PARAMETER; PERFORMANCE; PRIORITY JOURNAL; PROBLEM SOLVING; SIMULATION; WEIGHT;

EID: 0034284295     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(00)00310-6     Document Type: Article

References (26)

1. Solving inequality constrained combinatorial optimization problems by the Hopfield neural networks
2. Neural networks for shortest path computation and routing in computer networks
3. Traffic management of a satellite communication network using stochastic optimization
4. A theoretical investigation into the performance of the Hopfield model
5. Neural network for quadratic optimization with bound constraints
6. Neural Networks for Process Scheduling in Real-Time Communication Systems
7. Energy function analysis of dynamic programming neural networks
8. Extended Hopfield models for combinatorial optimization
9. Knapsack packing networks
10. Neural networks and physical systems with emergent collective computational properties
11. Neurons with graded response have collective computational properties like those of two-state neurons
12. Neural computations of decisions in optimization problems
13. Computing with neural networks: A model
14. Improving convergence and solution quality of hopfield-type neural networks with augmented lagrange multipliers
15. On solving constrained optimization problems with neural networks: A penalty method approach
16. Optimal Hopfield network for combinatorial optimization with linear cost function
17. An investigation into the improvement of local minima of the Hopfield network
18. Analysis of the relationship between weight parameters and stability of solutions in Hopfield networks from dynamic systems viewpoint
19. On the performance of hopfield network for graph search problem
20. Neural techniques for combinatorial optimization with applications
21. A high performance neural network for solving linear and quadratic programming problems
22. A new neural network for solving linear programming problems and its application