Combining multiple estimators has appeared as one of hot research topics in several areas including artificial neural networks. This paper presents three methods to work out the problem based on so-called softcomputing techniques, toward a unified framework of hybrid softcomputing techniques. The first method based on fuzzy logic nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. The second method based on genetic algorithm gives us an effective vehicle to determine the optimal weight parameters that are multiplied by the network outputs. Finally, we have proposed a hybrid synergistic method of fuzzy logic and genetic algorithm to optimally combine neural networks. The experimental results with the recognition problem of totally unconstrained handwritten digits show that the performance could be improved significantly with the proposed softcomputing techniques.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science Applications
- Computational Theory and Mathematics
- Artificial Intelligence