On-line identification and reconstruction of finite automata with generalized recurrent neural networks
| Autor: | Andrej Dobnikar, Ivan Gabrijel |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Finite-state machine
Dynamical systems theory Cognitive Neuroscience Deterministic finite automaton Recurrent neural network Artificial Intelligence Continuous spatial automaton Cluster Analysis Quantum finite automata Automata theory Nondeterministic finite automaton Nerve Net Algorithm Algorithms Mathematics |
| Zdroj: | Neural Networks. 16:101-120 |
| ISSN: | 0893-6080 |
| Popis: | In this paper finite automata are treated as general discrete dynamical systems from the viewpoint of systems theory. The unconditional on-line identification of an unknown finite automaton is the problem considered. A generalized architecture of recurrent neural networks with a corresponding on-line learning scheme is proposed as a solution to the problem. An on-line rule-extraction algorithm is further introduced. The architecture presented, the on-line learning scheme and the on-line rule-extraction method are tested on different, strongly connected automata, ranging from a very simple example with two states only to a more interesting and complex one with 64 states; the results of both training and extraction processes are very promising. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect