Recurrent neural networks

The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feedforward 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.
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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, longshort term memory (LSTM) networks, applications of LSTM networks.
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Recurrent Network có các hidden neuron: ph n t làm tầ ử rễ z1 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 )
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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.
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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, semisupervised and unsupervised training. Prof. Belic Igor’s chapter that deals with ANN application in modeling, illustrating two properties of ANN: universality and optimization. Prof.
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A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks A normalised version of the realtime 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 blockstochastic models is established. Finally, further discussion of recurrent neural network architectures is provided.
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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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DataReusing Adaptive Learning Algorithms In this chapter, a class of datareusing 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 datareusing algorithms outperforms the standard (a priori ) algorithms for nonlinear adaptive ﬁltering in terms of the instantaneous prediction error.
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Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks Perspective Optimisation of complex neural network parameters is a rather involved task. It becomes particularly diﬃcult for largescale networks, such as modular networks, and for networks with complex interconnections, such as feedback networks.
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Stability Issues in RNN Architectures Perspective The focus of this chapter is on stability and convergence of relaxation realised through NARMA recurrent neural networks. Unlike other commonly used approaches, which mostly exploit Lyapunov stability theory, the main mathematical tool employed in this analysis is the contraction mapping theorem (CMT), together with the ﬁxed point iteration (FPI) technique. This enables derivation of the asymptotic stability (AS) and global asymptotic stability (GAS) criteria for neural relaxive systems.
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Convergence of Online Learning Algorithms in Neural Networks An analysis of convergence of realtime algorithms for online learning in recurrent neural networks is presented. For convenience, the analysis is focused on the realtime recurrent learning (RTRL) algorithm for a recurrent perceptron. Using the assumption of contractivity of the activation function of a neuron and relaxing the rigid assumptions of the ﬁxed optimal weights of the system, the analysis presented is general and is applicable to a wide range of existing algorithms....
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In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman ﬁlter (NDEKF) algorithm. We now use this model to deal with highdimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network
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LEARNING SHAPE AND MOTION FROM IMAGE SEQUENCES Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Sue Becker and Ron Racine Department of Psychology, McMaster University, Hamilton, Ontario, Canada (beckers@mcmaster.ca) 3.1 INTRODUCTION In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman ﬁlter (NDEKF) algorithm. We now use this model to deal with highdimensional signals: moving visual images.
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In this chapter, we consider another application of the extended Kalman ﬁlter recurrent multilayer perceptron (EKFRMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.
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CHAOTIC DYNAMICS Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Simon Haykin Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada (haykin@mcmaster.ca) 4.1 INTRODUCTION In this chapter, we consider another application of the extended Kalman ﬁlter recurrent multilayer perceptron (EKFRMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.
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Tài liệu tham khảo về thuật toán tính Derivation of Delta Rules
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Since then Dr. Hans Berger discovered the electrical properties of the brain, it was considered ability to communicate outside personswith device only through the use of the brain wave (Vidal, 1973). Brain computer interface technology is aimed at communicating with users outside computer equipment through electroencephalographic signals as the command source (Wolpaw, JR, et al, 2000), (Birbaumer, N, et al, 2000).
112p lulanphuong 26032012 42 5 Download

Probabilistic accounts of language processing can be psychologically tested by comparing wordreading times (RT) to the conditional word probabilities estimated by language models. Using surprisal as a linking function, a signiﬁcant correlation between unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), but success using lexicalized models has been limited. In this study, phrase structure grammars and recurrent neural networks estimated both lexicalized and unlexicalized surprisal for words of independent sentences from narrative sources. ...
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