A long running debate about the architecture of the spoken-word recognition system has centered on the locus of lexical effects on phonemic processing: does lexical knowledge influence phoneme perception through feedback, or post-perceptually in a purely feedforward system? Elman and McClelland (1988) reported that lexically-restored ambiguous phonemes influenced the perception of the following...

This paper describes an approach to predict non-verbal cues from speech-related features. Our previous investigations of audiovisual speech showed that there are strong correlations between the two modalities. In this work we developed two models using different kinds of Recurrent Artificial Neural Networks: Elman and NARX, to predict parameters of activity for head motion using linguistic and ...

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...

This paper presents a Metahybrid algorithm that consists of the dual combination of Wolf Search (WS) and Elman Recurrent Neural Network (ERNN). ERNN is one of the most efficient feed forward neural network learning algorithm. Since ERNN uses gradient descent technique during the training process; therefore, it is not devoid of local minima and slow convergence problem. This paper used a new met...

The thermal error of the motorized spindle is an essential factor affecting machining accuracy high-speed numerically controlled machines. establishment a model for compensation can effectively improve impact errors on machine tool. This paper proposes sparrow search algorithm to optimize Elman neural network predict in spindles. First simulation analysis characteristics A02 spindle. Based resu...

This paper introduces an application of the Sequential Network Construction ( snc) method to select the size of several popular neural network predictor architectures for various benchmark training sets. The specific architectures considered are a fir network and the partially recurrent Elman network and its extension, with context units also added for the output layer. We consider an enhanceme...

From the viewpoint of applying recurrent neural networks to AI, it is not suitable to use learning algorithms based on optimum control theory. One reason of this problem is lack of correspondence between these algorithms and traditional symbol processing. In this report, we propose a new algorithm for modi ed Elman networks (PEX model). This algorithm is derived from the minimization procedure ...

The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the pa...

One of the well-known recurrent neural networks is the Elman network. Recently, it has been used in applications of system identification. The network has feedforward and feedback connections. It can be trained essentially as a feedforward network by means of the basic backpropagation algorithm, but its feedback connections have to be kept constant. For training success, it is important to sele...

In this paper, a Content Based Image Retrieval (CBIR) framework for medical images has been presented. An algorithm based on energy information obtained from Hilbert Transform for extracting features from medical images based on imaging modalities. The features are selected on the basis of the correlation among the extracted vectors depending upon the class label. An enhanced Genetic Algorithm ...