Title :
Relating logical neural network to conventional models of computation
Author :
Ludermir, Teresa B.
Author_Institution :
Dept. of Comput., King´´s Coll. London, UK
Abstract :
The author compares the class of languages that can be recognized by a logical neural network (LNN) with the classes of languages in Chomsky hierarchy. The computability of LNN is studied. The computation power of LNN is identical to the computation of a probabilistic automaton, that is, it is possible to recognize more than finite state languages with such machines. This indicates what can be expected from LNN, i.e. which functions this kind of network can learn
Keywords :
formal languages; neural nets; pattern recognition; Chomsky hierarchy; computability; language recognition; logical neural network; Computational modeling; Computer networks; Educational institutions; Learning automata; Neural networks; Pattern recognition; Probability; Random access memory; Read-write memory; Table lookup;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155320
