We use recurrent neural network (RNN) based language models to improve the BUT English meeting recognizer. On the baseline setup using the original language models we decrease word error rate (WER) more than 1% absolute by n-best list rescoring and language model adaptation. When n-gram language models are trained on the same moderately sized data set as the RNN models, improvements are higher ...

As the efficient calculation of eigenpairs of a matrix, especially, a general real matrix, is significant in engineering, and neural networks run asynchronously and can achieve high performance in calculation, this paper introduces a recurrent neural network (RNN) to extract some eigenpair. The RNN, whose connection weights are dependent upon the matrix, can be transformed into a complex differ...

A data center which consists of thousands of connected computer servers can be considered as a shared resource of processing capacity (CPU), memory, and disk space etc. The jobs arriving at the cloud data center are distributed to different servers via different paths. In addition, the internal traffic between servers inside the data center needs to be load balanced to multiple paths between th...

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and pre...

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice...

A parametric Recurrent Neural Network (RNN) model and an improved dynamic Back-propagation (BP) method of its learning are applied for real-time identification and state estimation of nonlinear plants. This RNN architecture has been expanded in a multimodel sense to identification of complex nonlinear plants. The obtained parameters of the RNN model are used for an adaptive control system desig...

Sign language gave hope to deaf and dumb people communicate with others. Although there is a significant development in sign language, recognition systems are created increase efficiency recognition. Our technology was aid communication for individuals suffering from hearing visibility issues. We have it using CNN (Convolutional neural network) RNN (Recurrent network). After pre-processing the ...

This paper presents a general recurrent neural network (RNN) model for online control of time-varying robot manipulators. The robot manipulators with different setting parameters work cooperatively on an unknown curve tracing. Each joint of the manipulator is respectively provided a learning method to optimize trajectory by the training RNN model. In this paper, the proposed RNN model shortens ...

In this paper, we propose a novel neural network model called RNN Encoder– Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability...

We describe a class of systems theory based neural networks called “Network Of Recurrent neural networks” (NOR), which introduces a new structure level to RNN related models. In NOR, RNNs are viewed as the high-level neurons and are used to build the high-level layers. More specifically, we propose several methodologies to design different NOR topologies according to the theory of system evolut...