Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural network architectures: an introduction
Neural network architectures: an introduction
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
A Novel Feature Recognition Neural Network and its Application to Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Design of Supervised Classifiers Using Boolean Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 14.98 |
It is shown that the Boolean-neural network can be used to solve NP-complete problems. The problem under consideration is the traveling salesman problem. The Boolean neural network has been modified to include the iterative procedure for solving combinatorial optimization problems. An architecture that utilizes this modified Boolean neural network (MBNN) is proposed for solving this problem. The simulation results have been found to be comparable to the simulated annealing algorithm (SAA), which is used as a test base. The MBNN implementation involves low hardware complexity, good noise immunity, and fast circuitry. This is very important in real-time systems and commercial job scheduling applications.