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检索条件"主题词=Backpropagation Algorithms"

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Customizing parallel formulations of backpropagation learning algorithm to neural network architectures: a summary of result

Customizing parallel formulations of backpropagation learnin...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： M.B. Amin S. Shekhar Department of Computer Science University of Minnesota Minneapolis MN USA

Several generic parallel formulations of the backpropagation learning algorithm have been proposed recently. Further speedups are possible by customizing parallel formulations to the architecture of the neural network. The paper addresses the issue of customizing parallel formulations of the backpropagation learning algorithm to a given neural network architecture on multiprocessors with hypercube-like communication topology. We introduce a new parallel formulation called rectangular checkerboarding which adapts to the network architecture and can provide performance gains for non-uniform neural networks, where the number of nodes vary across the layers. Algebraic analysis shows that each instance of rectangular checkerboarding (using a specific rectangular processor grid) is optimal for an important family of network architectures. Experiments on CM-5 show that customizing to network architecture can provide significant (/spl sim/50%) performance gains for many interesting non-uniform neural network architectures, which are currently used in important applications. We also introduce the staircase framework, which can use different processor grids for different layers of a neural network.< >

关键词： backpropagation algorithms Neural networks Concurrent computing Computer architecture Partitioning algorithms Broadcasting Hypercubes Shape Computer science Performance gain

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Truncated backpropagation through time and Kalman filter training for neurocontrol

Truncated backpropagation through time and Kalman filter tra...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： G.V. Puskorius L.A. Feldkamp Research Laboratory Ford Motor Company Limited Dearborn MI USA

We have recently established the feasibility of training recurrent neural networks by parameter-based decoupled extended Kalman filter (DEKF) algorithms for control of nonlinear dynamical systems. In this paper we investigate the use of truncated backpropagation through time (BPTT) for approximating the required dynamic derivatives that are used by the DEKF training algorithm. The use of this approximation allows the gradient calculations and weight updates by the DEKF algorithm to be performed asynchronously with application of control signals, thereby leading to a scalable, real-time, online training algorithm. We demonstrate in simulation the effectiveness of the BPTT-based DEKF algorithm for the problem of automotive engine idle speed control.< >

关键词： backpropagation algorithms Approximation algorithms Recurrent neural networks Nonlinear control systems Control systems Nonlinear dynamical systems Vehicle dynamics Automotive engineering Engines Velocity control

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Two adaptive stepsize rules for gradient descent and their application to the training of feedforward artificial neural networks

Two adaptive stepsize rules for gradient descent and their a...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Mohandes C.W. Codrington S.B. Gelfand School of Electrical Engineering Purdue University West Lafayette IN USA

Gradient descent, in the form of the well-known backpropagation algorithm, is frequently used to train feedforward neural networks, i.e. to find the weights which minimize some error measure /spl epsi/. Generally, the stepsize is fixed, and represents a compromise between stability and speed of convergence. In this paper, we derive two methods for adapting the stepsize and apply them to train neural networks on parity problems of various sizes.< >

关键词： Stability Neurons Neural networks Artificial neural networks backpropagation algorithms Convergence Joining processes Feedforward neural networks Time measurement Newton method

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Neural-like network model for color images analysis systems

Neural-like network model for color images analysis systems

引用

International Joint Conference on Neural Networks (IJCNN)

作者： D. Benitez-Diaz J. Carrabina M. Gonzalez-Rodriguez Departmento de informática y Sistemas. Edificio Department Informática y Matemáticas Universidad de Las Palmas de Gran Canaria Las Palmas Spain Centro Nacional de Microelectrònica Universitat Autònoma Barcelona Coruna Spain

In this paper we present the chromatic neural-like network. It is a two-layer network architecture used in an image analysis system to learn objects classification tasks. Each processing unit in the hidden layer is considered as a network which codifies the color information at pixel level. The output layer makes a features analysis from the hidden layer responses in every window of the image. The architecture and operation of this network are extracted from the studies of biologic visual neural systems behavior and it is a model to be used in an artificial vision system. The learning methodology is an unsupervised, fuzzy and adaptive one and lets a faster training for image processing than other algorithms. It combines the thresholding in features spaces technique and the fuzzy Kohonen clustering nets approach with a gaussian membership function. Experimental results of the net performance with real images are shown.< >

关键词： Image analysis Image color analysis Clustering algorithms Image coding Image processing Retina Biological system modeling Machine vision backpropagation algorithms Biology computing

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Comparison of four learning algorithms for multilayer perceptron with FIR synapses

Comparison of four learning algorithms for multilayer percep...

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International Joint Conference on Neural Networks (IJCNN)

作者： N. Benvenuto F. Piazza A. Uncini Dipartimento di Elettronica e Informatica Università di Padova Italy Dipartimento di Elettronica e Automatica Università di Ancona Italy

In recent years many training algorithms for dynamic neural networks have been proposed. As a matter of fact, it is well known that the exact training algorithm for dynamic networks, is non causal and can be implemented only in batch mode. In this paper we present a comparison of three online training algorithms for dynamic networks, where each synapsis is modelled by an FIR filter. In order to evaluate performance and computational complexity of the various algorithms, several computer simulations of dynamical system identifications have been carried out.< >

关键词： Multilayer perceptrons Finite impulse response filter backpropagation algorithms Neurons Neural networks Heuristic algorithms Multi-layer neural network Computer architecture Approximation algorithms Nonlinear dynamical systems

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Neural network for human cognitive state estimation

Neural network for human cognitive state estimation

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Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94)

作者： M. Takahashi O. Kubo M. Kitamura H. Yoshikawa Institute of Atomic Energy Kyoto University Kyoto Japan

A neural network (NN) has been applied to the human cognitive state estimation based on the set of physiological measures, heart rate, blood pressure, respiration rate, skin potential response (SPR), blink rate and saccadic eye motion rate have been chosen as the representative metrical indices reflecting human mental state. The qualitative tendencies of these measures have been taken as the inputs of the NN. The human cognitive states are categorized into six pre-specified states: (1) information acquisition (IA); (2) memory related (MR); (3) thought (TH); (4) motor action (MA); (5) emotion (EM); and (6) others (OT). The adopted network a is three layer feedforward network trained with a backpropagation algorithm with forgetting. Sets of training data for learning have been collected through laboratory experiments, in which the subjects were induced to undergo a specific sequence of cognitive states. The resultant NN showed superior capability of discriminating human cognitive states based on the pattern of the physiological measures.< >

关键词： Neural networks Humans State estimation Motion measurement Pressure measurement Heart rate Blood pressure Skin backpropagation algorithms Training data

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Layered neural networks with horizontal connections can reduce the number of units

Layered neural networks with horizontal connections can redu...

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International Joint Conference on Neural Networks (IJCNN)

作者： J. Smid Department of Mathematics Morgan State University Baltimore MD USA

We define layered networks with horizontal connections as networks having units that receive inputs from the lower layer and also from the previous units of the same layer. We show that architecture with horizontal connections does not require as many units in the hidden layers as the plain layered architecture in order to approximate a function.< >

关键词： Neural networks backpropagation algorithms Computer architecture Computer networks Nonhomogeneous media Pursuit algorithms Strips Mathematics Data systems Spirals

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A discriminative training algorithm for predictive neural network models

A discriminative training algorithm for predictive neural ne...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： KyungMin Na JaeYeol Rheem SouGuil Ann Department of Electronics Engineering Seoul National University Seoul South Korea

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models, however, suffer from poor discrimination between acoustically similar speech signals. In this paper, we propose a new discriminative training algorithm for predictive neural network models based on the generalized probabilistic descent (GPD) algorithm and the minimum classification error formulation. The proposed algorithm allows direct minimization of a recognition error rate. Evaluation of our training algorithm on Korean digits shows its effectiveness by 30% reduction of recognition error.< >

关键词： Prediction algorithms Neural networks Predictive models backpropagation algorithms Speech recognition Dynamic programming Training data Artificial neural networks Acoustical engineering Power engineering and energy

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Ternary synaptic weights algorithm: neural network training with don't care attributes

Ternary synaptic weights algorithm: neural network training ...

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International Symposium on Speech, Image Processing and Neural Networks

作者： F. Ulgen N. Akamatsu Justsystem Corporation Tokushima Japan University of Tokushima Tokushima Japan

Classification systems, which have long been realized through rule-based systems, are now being implemented with neural-expert system hybrids because of the well known difficulties associated with the formation of rules and the ease with which neural networks can capture the relationships between attributes and classifications. backpropagation, which is a commonly used neural network training algorithm, suffers from slow training and the possibility of local minima trapping. In this paper we propose a new neural network training algorithm, ternary synaptic weights (TSW) algorithm, which offers fast, guaranteed learning and automatic topology determination. Also, it provides an insight into the interrelationships between the attributes and the classifications, which is very suitable for hybridization of neural networks and expert systems. Furthermore, it takes don't care attributes, which are an essential part of rule-based systems, into consideration during training.< >

关键词： Neural networks backpropagation algorithms Network topology Knowledge based systems Expert systems Classification tree analysis Bayesian methods Decision trees Error analysis Fuzzy reasoning

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Scaling properties of on-line learning with momentum

Scaling properties of on-line learning with momentum

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International Joint Conference on Neural Networks (IJCNN)

作者： T. Heskes W. Wiegerinck A. Komoda Beckman Institute and Department of Physics University of Illinois Urbana IL USA Department of Medical Physics and Biophysics University of Nijinegen Toernooiveld Nijmegen Netherlands

We study online learning with momentum term for nonlinear learning rules. Through introduction of auxiliary variables, we show that the learning process can still be described by a first-order Markov process. For small learning parameters /spl eta/ and momentum parameters /spl alpha/ close to 1 (we consider the case /spl alpha/=1-/spl radic/(/spl eta///spl lambda/) for small /spl eta/), Van Kampen's expansion can be applied in a straightforward manner. We obtain evolution equations for the average network state and the fluctuations around this average. These evolution equations depend (after rescaling of time and fluctuations) only on /spl lambda/=/spl eta//(1-/spl alpha/)/sup 2/: all combinations (/spl eta/,/spl alpha/) with the same value of /spl lambda/ give rise to similar graphs. For small /spl lambda/, i.e., /spl eta//spl Lt/(1-/spl alpha/)/sup 2/, learning with momentum term is equivalent to learning without momentum term with rescaled learning parameter /spl eta//spl tilde/=/spl eta//(1-/spl alpha/). Simulations with the nonlinear Oja learning rule confirm our theoretical results.< >

关键词： Fluctuations Differential equations Physics Biophysics Markov processes Least squares approximation backpropagation algorithms Nonlinear equations Stochastic processes Time measurement

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