Intravenous transfusion is resorted to in various conditions of malnutrition, hypoproteinaemia and in surgery requiring intravenous alimentation (Elman, 1952; Levy and Siller, 1942; Krishnan, 1944). Being a preparation derived from proteins, several tests for its preparation, freedom from toxicity, and antigenic reactions are laid down in the pharmacopoeia (cf. Basu, Bose and Sen, 1946; U. S. P...

License plate recognition system plays an important role in many applications. An automatic recognizer for Iraqi License Plates using ELMAN Neural network is proposed in this manuscript. The processing procedures are developed in several stages. Experimental results are reported in the end of the paper to illustrate the performance of the proposed method.

The TRACE model of speech perception (McClelland & Elman, 1986) is used to simulate graded sensitivity to mispronunciations of familiar words as reported by White and Morgan (2008). Our simulations predict that phoneme or lexical competition may be absent in the mental lexicons of the 19month-old infants tested experimentally.

In this paper we propose a nonlinear scalar predictor based on a combination of Multi Layer Perceptron, Radial Basis Functions and Elman networks. This system is applied to speech coding in an ADPCM backward scheme. The combination of this predictors improves the results of one predictor alone. A comparative study of this three neural networks for speech prediction is also presented.

This paper presents a training model for Elman recurrent neural networks, based on evolutionary algorithms. The proposed evolutionary algorithms are classic genetic algorithms, the multimodal clearing algorithm and the CHC algorithm. These training algorithms are compared in order to assess the effectiveness of each training model when predicting Spanish autonomous indebtedness.

Starting from an GMRES error estimate proposed by Elman in terms of the ratio of the smallest eigenvalue of the hermitian part and the norm of some non-symmetric matrix, we propose some asymptotically tighter bound in terms of the same ratio. Here we make use of a recent deep result of Crouzeix et al. on the norm of functions of matrices.

The paper is devoted to time series prediction using linear, perceptron and Elman neural networks of the proposed pattern structure. Signal wavelet de-noising in the initial stage is discussed as well. The main part of the paper is devoted to the comparison of different models of time series prediction. The proposed algorithm is applied to the real signal representing gas consumption.

We consider the problem of learning a finite automaton with recurrent neural networks, given a training set of sentences in a language. We train Elman recurrent neural networks on the prediction task and study experimentally what these networks learn. We found that the network tends to encode an approximation of the minimum automaton that accepts only the sentences in the training set.

The back-propagation (BP) training scheme is widely used for training network models in cognitive science besides its well known technical and biological short-comings. In this paper we contribute to making the BP training scheme more acceptable from a biological point of view in cognitively motivated prediction tasks overcoming one of its major drawbacks. Traditionally, recurrent neural networ...