Simple recurrent networks were trained with sequences of phonemes from a corpus of Turkish words. The network's task was to predict the next phoneme. The aim of the study was to look at the representations developed within the hidden layer of the network in order to investigate the extent to which such networks can learn phonological regularities from such input. It was found that in the diiere...

This paper describes a class of recurrent neural networks related to Elman networks. The networks used herein Figure 1: Architecture of Elman's recurrent network; ω differ from standard Elman networks in that they may signifies total interconnection with trainable weights; 1 have more than one state vector. Such networks have an signifies that the activations at the destination are a explicit r...

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.

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.

Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to tackle a simple pseudo-natural machine tr...

Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to tackle a simple pseudo-natural machine tr...

Precise and reliable monthly runoff prediction plays a vital role in the optimal management of water resources, but nonstationarity skewness time series can pose major challenges for developing appropriate models. To address these issues, this paper proposes novel hybrid model by introducing variational mode decomposition (VMD) Box–Cox transformation (BC) into Elman neural network (Elman), name...

In this paper we present a phoneme recognition system based on the Elman predictive neural networks. The recurrent neural networks are used to predict the observation vectors of speech frames. Recognition of phonemes is done using the prediction error as distortion measure in the Viterbi algorithm. The performance of the neural predictive networks is evaluated on both the training database and ...

Recurrent neural networks, especially for Elman Neural Network, have attracted the attention of researchers in the fields of Dynamic System Identification (DSI) since they took the memory unit through the context delay. In this paper, we propose an Adaptive Local Search (ALS) algorithm to train Elman Neural Network (ENN) for Dynamic Systems Identification (DSI) from a new angle instead of tradi...