Introduction Simple recurrent networks (SRNs) are able to learn and represent lexical classes (Elman, 1990) and grammatical knowledge, such as agreement and argument structure (Elman, 1991), on the basis of co-occurrence regularities embedded in simple and complex sentences. In the present study, we address the question whether SRNs can represent differences in the thematic roles assigned by ve...

In this paper we examine Elman’s position (1999) on generalization in simple recurrent networks. Elman’s simulation is a response to Marcus et al.’s (1999) experiment with infants; specifically their ability to differentiate between novel sequences of syllables of the form ABA and ABB. Elman contends that SRNs can learn to generalize to novel stimuli, just as Marcus et al’s infants did. However...

Original Elman, which is one of the well-known dynamic recurrent neural network (DRNN), has been improved to easily apply in dynamic systems identification during the past decade. In this paper, a learning algorithm for Original Elman neural networks (ENN) based on modified particle swarm optimization (MPSO), which is a swarm intelligent algorithm (SIA), is presented. MPSO and Elman are hybridi...

We present results of experiments with Elman recurrent neural networks (Elman, 1990) trained on a natural language processing task. The task was to learn sequences of word categories in a text derived from a primary school reader. The grammar induced by the network was made explicit by cluster analysis which revealed both the representations formed during learning and enabled the construction o...

Active Noise Control (ANC) works on the principle of destructive interference between the primary disturbance field heard as undesired noise and the secondary field which is generated from control actuators. In the simplest system, the disturbance field can be a simple sine wave, and the secondary field is the same sine wave but 180 degrees out of phase. This research presents an investigation ...

watershed outflow has influenced by different factors such as climatic, human and physical aspects and this variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. also, some remote meteorological signals can cause changes in meteorological conditions in different regions. hab...

We present preliminary results of experiments with two types of recurrent neural networks for a natural language learning task. The neural networks, Elman networks and Recurrent Cascade Correlation (RCC), were trained on the text of a first-year primary school reader. The networks performed a one-step-look-ahead task, i.e. they had to predict the lexical category of the next following word. Elm...

Although returns distributions are complex, they can’t avoid manipulation in any form. We propose a new methodology, the Intelligent Portfolio Selection & Optimisation System – IPSOS that takes into account hidden information within the extended accounting data and financial statements, among other values incorporates them on a new Jordan Elman hybrid network to provide safer financial evaluati...