Elman network is a class of recurrent neural networks used for function approximation. The main problem of this class is that its structure has a set of global sigmoid functions at its hidden layer. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This paper introduces a new version of the Elman n...

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 ...

After the major reasons of water bloom were analyzed, using the rough set theory and principal component analysis respectively to identify the main factors affecting the forecast algal blooms. On this basis, to take advantage of the Libsvm water bloom prediction model and Elman water bloom prediction model for the shortterm prediction of algal blooms phenomenon respectively. Obtained through th...

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification” of the time series under stu...

Breast cancer is the second leading cause of cancer deaths worldwide, occurs in one out of eight women .still there is no known way of preventing this pathology. Early detection of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Network is a branch of Artificial intelligence, has been accepted as a new technology in computer science...

Simple recurrent networks (SRNs) in symbolic time-series prediction (e.g., language processing models) are frequently trained with gradient descent--based learning algorithms, notably with variants of backpropagation (BP). A major drawback for the cognitive plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer. Yet agents in natur...

In this paper, we investigate two different recurrent neural network (RNN) architectures: Elman RNN and recently proposed clockwork RNN [1] for statistical parametric speech synthesis (SPSS). Of late, deep neural networks are being used for SPSS which involve predicting every frame independent of the previous predictions, and hence requires post-processing for ensuring smooth evolution of speec...

In the modeling of vision systems of biological organisms, one of the important features is the ability to sense motion (BorgGraham et al. 1992, Huntsberger 1995, Klauber 1997, Missler and Kamangar 1995, Rosenberg and Ariel 1991). Motion is sensed by animals through neurons that receive input over some area of the field of view (Newman et al. 1982 and Rosenberg and Ariel 1991). For such a neuro...

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 ...

In this paper, an artificial intelligent tool is proposed using fuzzy logic (FL) and recurrent neural networks (RNN) for definition and forecast of patient’s clinical condition. The fuzzy logic-based proposed first phase of the tool permits the analysis of the current state of the patient, which allows the training of the artificial neural network. In the second phase, two Elman networks Multi ...