This paper presents an investigation on the trajectory control of a robot using a new type of recurrent neural network. A three-layered recurrent neural network is employed to estimate the forward dynamics model of the robot. Standard backpropagation (BP) algorithm is used as a learning algorithm for this network to minimise the difference between the robot actual response and that predicted by...

Syntactic rules in human language usually refer to the hierarchical structure of sentences. However, the input during language acquisition can often be explained equally well with rules based on linear order. The fact that children consistently ignore these linear explanations to instead settle on hierarchical explanations has been used to argue for an innate hierarchical bias in humans. We rev...

In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evol...

In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs) with an attention mechanism. We show that an Attentive RNN-LM (with 14.5M parameters) achieves a better perplexity than larger RNN-LMs (with 66M parameters) and achieves performance comparable to an ensemble of 10 similar sized RNN-LMs. We also show that an Attentive RNN-LM needs less contextual information to achieve s...

We compare the performance of a recurrent neural network with the best results published so far on phoneme recognition in the TIMIT database. These published results have been obtained with a combination of classifiers. However, in this paper we apply a single recurrent neural network to the same task. Our recurrent neural network attains an error rate of 24.6%. This result is not significantly...

We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our mode...

In Chinese societies where superstition is of paramount importance, vehicle license plates with desirable numbers can fetch for very high prices in auctions. Unlike auctions of other valuable items, however, license plates do not get an estimated price before auction. In this paper, I construct a deep recurrent neural network to predict the prices of vehicle license plates in Hong Kong based on...

The paper deals with a discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the considered network is a locally recurrent globally feedforward. In the paper, conditions for global stability of the neural network considered are derived using the Lyapunov’s second method.