Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems

Equivalence in Knowledge Representation: Automata, Recurrent Neural Networks, and Dynamical Fuzzy Systems

Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...

Equivalence in Knowledge Representation : Automata , Recurrent Neural Networks , andDynamical Fuzzy

Neuro-fuzzy systems-the combination of artiicial neural networks with fuzzy logic-are becoming increasingly popular. However, neuro-fuzzy systems need to be extended for applications which require context (e.g., speech, handwriting, control). Some of these applications can be modeled in the form of nite-state automata. Previously, it was proved that deterministic nite-state automata (DFAs) can ...

Commonsense knowledge representation and reasoning with fuzzy neural networks

Recurrent Neural Networks and Finite Automata

This article studies finite size networks that consist of interconnections of synchronously evolving processors. Each processor updates its state by applying an activation function lo a linear combination of the previous states of all units. We prove that any function for which the left and right limits exist and are different can be applied to the neurons to yield a network which is at least a...

Prediction of Dynamical Systems by Recurrent Neural Networks

Recurrent neural networks in general achieve better results in prediction of time series then feedforward networks. Echo state neural networks seem to be one alternative to them. I have shown on the task of text correction, that they achieve slightly better results compared to already known method based on Markov model. The major part of this work is focused on alternatives to recurrent neural ...