Recurrent neural networks

Xem 1-20 trên 47 kết quả Recurrent neural networks
  • This paper proposes a system for classifying audio scene that is based on Gated Recurrent Neural Network. The system includes two main parts - feature extraction and classification.

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  • The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.

    pdf112p cucdai_1 20-10-2012 60 12   Download

  • A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modelling and forecasting. The performance of different neural networks in groundwater level forecasting was examined in order to identify an optimal ANN model for groundwater level forecast. The Devasugur nala watershed was selected for the study, located at northern part of Raichur district Karnataka and comes under middle Krishna river basin.

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  • The research of neural networks has experienced several ups and downs in the 20th century. The last resurgence is believed to be initiated by several seminal works of Hopfield and Tank in the 1980s, and this upsurge has persisted for three decades. The Hopfield neural networks, either discrete type or continuous type, are actually recurrent neural networks (RNNs). The hallmark of an RNN, in contrast to feedforward neural networks, is the existence of connections from posterior layer(s) to anterior layer(s) or connections among neurons in the same layer....

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  • This lecture introduces you sequence models. The goal is for you to learn about: Recurrent neural networks, the vanishing and exploding gradients problem, long-short term memory (LSTM) networks, applications of LSTM networks.

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  • This paper deals with an identification model control system using recurrent neural networks to estimate the angle main mirror in azimuth moving of large radio telescope electric servo drive. The architectural approached to design recurrent neural networks based on “Nonlinear Auto Regressive with Exogenous inputs – NARX models” is analyzed. It is convenient to apply this design in the field of prediction and modeling control system.

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  • This paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.

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  • Recurrent Network có các hidden neuron: ph n t làm tầ ử rễ z-1 được dùng Đầu ra của Neural được feedback về tất cả các Neural. Recurrent Neural Network (RNN) Input: Pattern (thường có nhiều hoặc xuống cấp) Output: Corresponding pattern (hoàn hảo/xét môṭ cách tương đôí la ̀ ko có nhiễu )

    ppt53p haiph37 15-09-2010 78 24   Download

  • Bài giảng "Máy học nâng cao: Deep learning an introduction" cung cấp cho người học các kiến thức: Introduction, applications, convolutional neural networks and recurrent neural networks, hardware and software. Mời các bạn cùng tham khảo nội dung chi tiết.

    pdf109p abcxyz123_08 11-04-2020 19 1   Download

  • Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR.

    pdf11p viwyoming2711 16-12-2020 8 1   Download

  • Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m.

    pdf11p kethamoi6 01-07-2020 4 0   Download

  • Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.

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  • In this study, a combination of backstepping technique, double recurrent fuzzy wavelet based on neural networks (DRFWBONNs), adaptive sliding mode controller (ASMC), and adaptive proportional-integral (API) control with dead-zone friction is introduced to the industrial robot manipulator (IRM). Simulation results show the high performance of this control method when compared to adaptive-fuzzy (AF) and proportional-integral-derivative (PID) controller.

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  • The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion.

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  • This section illustrates some general concepts of artificial neural networks, their properties, mode of training, static training (feedforward) and dynamic training (recurrent), training data classification, supervised, semi-supervised and unsupervised training. Prof. Belic Igor’s chapter that deals with ANN application in modeling, illustrating two properties of ANN: universality and optimization. Prof.

    pdf302p bi_bi1 09-07-2012 89 25   Download

  • This book presents biologically inspired walking machines interacting with their physical environment. It describes how the designs of the morphology and the behavior control of walking machines can benefit from biological studies.

    pdf194p nhatro75 16-07-2012 82 17   Download

  • A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks A normalised version of the real-time recurrent learning (RTRL) algorithm is introduced. This has been achieved via local linearisation of the RTRL around the current point in the state space of the network. Such an algorithm provides an adaptive learning rate normalised by the L2 norm of the gradient vector at the output neuron. The analysis is general and also covers simpler cases of feedforward networks and linear FIR filters...

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  • Recurrent Neural Networks Architectures Perspective In this chapter, the use of neural networks, in particular recurrent neural networks, in system identification, signal processing and forecasting is considered. The ability of neural networks to model nonlinear dynamical systems is demonstrated, and the correspondence between neural networks and block-stochastic models is established. Finally, further discussion of recurrent neural network architectures is provided.

    pdf21p doroxon 12-08-2010 81 13   Download

  • Neural Networks as Nonlinear Adaptive Filters Perspective Neural networks, in particular recurrent neural networks, are cast into the framework of nonlinear adaptive filters. In this context, the relation between recurrent neural networks and polynomial filters is first established. Learning strategies and algorithms are then developed for neural adaptive system identifiers and predictors. Finally, issues concerning the choice of a neural architecture with respect to the bias and variance of the prediction performance are discussed....

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  • Data-Reusing Adaptive Learning Algorithms In this chapter, a class of data-reusing learning algorithms for recurrent neural networks is analysed. This is achieved starting from a case of feedforward neurons, through to the case of networks with feedback, trained with gradient descent learning algorithms. It is shown that the class of data-reusing algorithms outperforms the standard (a priori ) algorithms for nonlinear adaptive filtering in terms of the instantaneous prediction error.

    pdf14p doroxon 12-08-2010 76 9   Download



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