Warm start by Hopfield neural networks for interior point methods
Computers and Operations Research
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
Implementing soft computing techniques to solve economic dispatch problem in power systems
Expert Systems with Applications: An International Journal
Neuro-immune approach to solve routing problems
Neurocomputing
Fast hopfield neural networks using subspace projections
Neurocomputing
A study of the transiently chaotic neural network for combinatorial optimization
Mathematical and Computer Modelling: An International Journal
Four-Quadrant division with HNN for euclidean TSP
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
An analysis is made of the behavior of the Hopfield model as a content-addressable memory (CAM) and as a method of solving the traveling salesman problem (TSP). The analysis is based on the geometry of the subspace set up by the degenerate eigenvalues of the connection matrix. The dynamic equation is shown to be equivalent to a projection of the input vector onto this subspace. In the case of content-addressable memory, it is shown that spurious fixed points can occur at any corner of the hypercube that is on or near the subspace spanned by the memory vectors. Analysed is why the network can frequently converge to an invalid solution when applied to the traveling salesman problem energy function. With these expressions, the network can be made robust and can reliably solve the traveling salesman problem with tour sizes of 50 cities or more