Article
Refine
Year of publication
- 2000 (2) (remove)
Document Type
- Article (2) (remove)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Institute
- Informatik (2) (remove)
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented.
We present a theoretical analysis of structural FSM traversal, which is the basis for the sequential equivalence checking algorithm Record & Play presented earlier. We compare the convergence behaviour of exact and approximative structural FSM traversal with that of standard BDD-based FSM traversal. We show that for most circuits encountered in practice exact structural FSM traversal reaches the fixed point as fast as symbolic FSM traversal, while approximation can significantly reduce in the number of iterations needed. Our experiments confirm these results.