A Learning Multiple-Valued Logic Network: Algebra, Algorithm, and Applications
IEEE Transactions on Computers
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Circuits for multiple valued logic—a tutorial and appreciation
MVL '76 Proceedings of the sixth international symposium on Multiple-valued logic
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Design of Multivalued Circuits using Genetic Algorithms
ISMVL '96 Proceedings of the 26th International Symposium on Multiple-Valued Logic
Logical not Polynomial Forms to represent Multiple-Valued Functions
ISMVL '96 Proceedings of the 26th International Symposium on Multiple-Valued Logic
A Minimization Technique for Multiple-Valued Logic Systems
IEEE Transactions on Computers
Logic Design of Multivalued I2L Logic Circuits
IEEE Transactions on Computers
Automated Design of Multiple-Valued Logic Circuits by Automatic Theorem Proving Techniques
IEEE Transactions on Computers
Automated Synthesis of Combinational Logic Using Theorem-Proving Techniques
IEEE Transactions on Computers
A Many-Valued Algebra for Switching Systems
IEEE Transactions on Computers
Multiple-Valued Logic its Status and its Future
IEEE Transactions on Computers
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, we propose a stochastic dynamic local search (SDLS) method for Multiple-Valued Logic (MVL) learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary error increases during learning. Thus the network may eventually reach the global minimum state or its best approximation with very high probability. Simulation results show that the proposed algorithm has the superior abilities to find the global minimum for the MVL network learning within reasonable number of iterations.