T.B. Ludermir
Synopsis
The performance of a learning algorithm is measured by looking at the structure achieved through such learning processes and comparing the desired function f to the function computed by the network acting as a classical automaton. It is important to characterise the functions which can be computed by the network in this fixed structure, since if there is no configuration which allows the computation of f, then a network cannot learn to compute f. We studied the computability of networks of PLNs (Probabilistic Logic Node (Aleksander, 1988)). We suggested a new method of recognition based on stored probabilities with PLN networks. This new method increases the computability power of such networks beyond that of finite state acceptors. We proved that the computability of a PLN network is identical to the computability of a probabilistic automaton (Rabin, 1963). This implies that it is possible to recognise more than finite state languages with such machines.
Key words:
computability, finite state machine, logical neural network, probabilistic automaton, weighted regular language.