It is commonly assumed that innate linguistic constraints are necessary to learn a natural language, based on the apparent lack of explicit negative evidence provided to children and on Gold's proof that, under assumptions of virtually arbitrary positive presentation, most interesting classes of languages are not learnable. However, Gold's results do not apply under the rather common assumption...

We explore a network architecture introduced by Elman (1988) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t-l, together with element t, to predict element t+ 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the gram...

We explore the idea of adding the ability to learn time dependent tasks into the Intelligent Adaptive Curiosity (IAC) learning scheme as presented by Oudeyer, et al. [2004, 2006]. We use a three layer architecture (IGE) consisting of a bottom-level IAC brain, an Elman network, and a toplevel IAC brain. For both IAC brains we use the equilibrium Growing Neural Gas (GNG) algorithm to cluster the ...

Sensor networks have helped wireless communication systems. Over the last decade, researchers focused on energy efficiency in sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using ...

Abstract Aiming at the time-varying and fluctuating characteristics of photovoltaic power output, a GA-Elman model is proposed for short-term prediction active power. This can use historical value daily meteorological information to directly forecast power, which convenient dispatching department supply bureau obtain output data in advance enhance reliability supply. Firstly, input variables El...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TDDBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was propos...

Elman Neural Network has been an efficient system identification tool in many areas. However, one of the problems often associated with this type of network is that the speed of learning is too slow. The HF Elman neural network is presented for the modeling of unknown delay and high-order nonlinear system. Then chaos searching is imported to train it, make BP algorithm can skip the local minimu...