Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A ...

We consider a system based on an Elman network for a categorization task. Four objects are investigated by an automa walking around in circles. The shapes are derived from four version of a cross: square, thick cross, critical cross and thin cross. Therefore, the input of the system is represented by the distance-wave relieved by the sensor at each step. We let several parameters vary: starting...

Multilayer perceptron network (MLP), FIR neural network and Elman neural network were compared in four different time series prediction tasks. Time series include load in an electric network series, fluctuations in a far-infrared laser series, numerically generated series and behaviour of sunspots series. FIR neural network was trained with temporal backpropagation learning algorithm. Results s...

Connectionism, Parallel Distributed Processing (PDP), or neural networks have had a profound impact on cognitive sciences in the last two decades. Language, as one of the central human cognitive components, has received in-depth treatments since the beginning of connectionist research. The acquisition of the English past tense (Rumelhart & McClelland, 1986), the recognition of speech (McClellan...

The prediction and modeling of dynamical systems, for example chaotic time series, with neural networks remains an interesting and challenging research problem. It seems to be rather natural to employ recurrent neural networks for which we will suggest a new structure based on the Elman net [1]. The major di erence to neural networks as proposed by Williams and Zipser [2] is the way we organize...

Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic syste...

In this work the task of classifying natural language sentences using recurrent neural networks is considered. The goal is the classification of the sentences as grammatical or ungrammatical. An acceptable classification percentage was achieved, using encoded natural language sentences as examples to train a recurrent neural network. This encoding is based on the linguistic theory of Government...

The classification, selection and organization of electronic messages (e-mail) is a task that can be supported by a neural information processing system. The objective is to select those incoming messages for display that are most important for a particular user, and to propose actions in anticipation of the user´s decisions. The artificial neural networks (ANNs) extract relevant information fr...