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检索条件"主题词=Backpropagation Algorithms"

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Design of the adaptive noise canceler using neural network with backpropagation algorithm

Design of the adaptive noise canceler using neural network w...

引用

International Forum on Strategic Technology, IFOST

作者： Hyung Suk Chu Chong Koo An DSP Laboratory Department of Electronics Eng University of Ulsan Ulsan South Korea

In this paper, an adaptive noise canceller using a neural network with backpropagation is designed. The adaptive noise canceller using the least mean square algorithm has the large correlativity of the reference signal and shows the limitation of the big signal to noise ratio. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceller is compared with that of the system which is used the least mean square algorithm.

关键词： Algorithm design and analysis Noise cancellation Neural networks Signal to noise ratio Signal processing Least mean square algorithms Adaptive filters Signal processing algorithms Adaptive signal processing backpropagation algorithms

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A backpropagation algorithm for multilayer hybrid order statistic filters

A backpropagation algorithm for multilayer hybrid order stat...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： F. Palmieri Department of Electrical and Systems Engineering University of Connecticut Storrs CT USA

A hybrid linear order statistic (HLOS) filter is a network of interconnected linear combiners and rank-forming blocks. This structure generalizes most of the previously proposed order statistic filters, including the median filter. The author derives a simple algorithm to compute the coefficients of the filter adaptively and reports a successful simulation of the restoration of a square wave corrupted by non-Gaussian noise. It is noted that HLOS filters show great potential in applications such as image processing and filtering of signals of nonstrictly stationary nature.< >

关键词： backpropagation algorithms Nonhomogeneous media Statistics Nonlinear filters Adaptive filters Computational modeling Image restoration Signal restoration Image processing Filtering

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One-lead ischemia detection using a new backpropagation algorithm and the European ST-T database

One-lead ischemia detection using a new backpropagation algo...

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Computers in Cardiology (CinC)

作者： T. Stamkopoulos M. Strintzis C. Pappas N. Maglaveras Aristotle University of Thessaloniki Macedonia Greece

A supervised neural network (NN) based algorithm was used to detect ischemic episodes from electrocardiograms (ECGs). The algorithm is tested on the European ST-T database. The algorithm is very fast in its recall state due to the NN, and uses the minimum amount of information, since it is applied on a one-lead ECG. The adaptive training backpropagation algorithm reduces dramatically the training time, and makes possible adjustment training. Even though the algorithm has some problems with detecting the exact onset and end of an ischemic episode, its performance was encouraging since it had a gross sensitivity of 84.4% for ischemia episode detection in the 60 out of 90 records on which it was initially tested. Thus, it seems to be suitable for use in critical care units due to its speed and training capabilities.< >

关键词： Ischemic pain backpropagation algorithms Databases Neural networks Electrocardiography Testing Detectors Blood Myocardium Biomembranes

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Hardware implementation of backpropagation algorithm based on CHEMFET sensor selectivity

Hardware implementation of backpropagation algorithm based o...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Nurhakimah Abd Aziz Muhammad Al Kasyaf Abdul Latif Wan Fazlida Hanim Abdullah Nooritawati Md Tahir Maizatul Zolkapli Faculty of Electrical Engineering Universiti Teknologi MARA (UiTM) Shah Alam Selangor Malaysia Universiti Teknologi MARA Shah Alam Selangor MY

In this study, modeling approach for interpretation of data logged from chemically field-effect transistor (CHEMFET) sensor is described. Firstly, backpropagation algorithm is used to train the proposed network by optimizing the parameters of the network. Then, by applying the optimized parameters obtained from the trained network, the feed forward neural network algorithm is implemented using C language for compatibility with 16-bit microcontroller board and the output is compared with the simulation output which has been simulated using MATLAB software. Initial findings showed that the neural the proposed method is able to provide excellence estimation of main ion concentration in mixed solution as well as capable to interpret and estimate the ion concentration in mixed solution.

关键词： Neural networks Microcontrollers Hardware Estimation backpropagation algorithms Conferences Feeds

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A dynamic backpropagation algorithm with application to gain-scheduled aircraft flight control system design

A dynamic backpropagation algorithm with application to gain...

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Australian and New Zealand Conference on Intelligent Information Systems

作者： Jianliang Wang Weiqiang Zhang School of EEE Nanyang Technological University Singapore Singapore

ISBN: (纸本)0818682183

The authors introduce a dynamic backpropagation algorithm for continuous-time dynamic neural fuzzy systems, as a generalization of the standard backpropagation algorithm for feedforward neural network systems. The proposed algorithm is applied to the design and training of a fuzzy-gain-scheduler for an aircraft flight control system. The trained control system is tested on a full-fledged six-degree-of-freedom nonlinear aircraft simulation package. Simulation results show that significant improvement is achieved through training of the fuzzy-gain-scheduler by using the proposed dynamic backpropagation algorithm.

关键词： backpropagation algorithms Aerospace control Nonlinear dynamical systems Aircraft Fuzzy systems Neural networks Feedforward neural networks Algorithm design and analysis Control system synthesis Nonlinear control systems

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The manifold backpropagation algorithm

The manifold backpropagation algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Puechmorel M.I. Iahla F. Castanie ENSEEIHT GAPSE Toulouse France Ecole Nationale Superieure d'Electrotechnique d'Electronic d'Informatique d'Hydraulique et des Telecommunications Toulouse Midi-PyrÃ©nÃ©es FR ENSEEIHT Toulouse France

This paper introduces a generalization of the concept of neural network by allowing the activation functions to be defined from a C/sup k/-manifold to a C/sup k/-manifold. Real-valued neural networks (RNN), complex-valued neural networks (CNN) as well as the recently introduced vector neural networks (VNN) can be seen as special cases of manifold neural networks (MNN). The classical back-propagation algorithm can be extended to manifolds, assuming some hypothesis on the order of differentiability and on some global properties of the manifold, The case of analytic manifolds are briefly discussed as well as the case of manifolds with boundaries.< >

关键词： backpropagation algorithms Neural networks Vectors Recurrent neural networks Cellular neural networks Neurons Function approximation Multi-layer neural network Algorithm design and analysis Adaptive filters

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On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning

On derivation of stagewise second-order backpropagation by i...

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International Joint Conference on Neural Networks (IJCNN)

作者： E. Mizutani S. Dreyfus Department of Computer Science Tsinghua University Hsinchu Taiwan Department of Industrial Engineering and Operations Research University of California CA USA

We present a simple, intuitive argument based on "invariant imbedding" in the spirit of dynamic programming to derive a stagewise second-order backpropagation (BP) algorithm. The method evaluates the Hessian matrix of a general objective function efficiently by exploiting the multistage structure embedded in a given neural-network model such as a multilayer perceptron (MLP). In consequence, for instance, our stagewise BP can compute the full Hessian matrix "faster" than the standard method that evaluates the Gauss-Newton Hessian matrix alone by rank updates in nonlinear least squares learning. Through our derivation, we also show how the procedure serves to develop advanced learning algorithms; in particular, we explain how the introduction of "stage costs" leads to alternative systematic implementations of multi-task learning and weight decay.

关键词： Costs Optimal control backpropagation algorithms Equations Dynamic programming Multilayer perceptrons Gaussian processes Least squares methods Neural networks Multi-layer neural network

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An update function that speeds up backpropagation learning

An update function that speeds up backpropagation learning

引用

International Joint Conference on Neural Networks (IJCNN)

作者： W. El Deredy N.M. Branston Department of Neurological Surgery Institute of Neurology London UK

We consider a modification of the backpropagation (BP) learning algorithm in which a linear function, directly proportional to the deviation between target values and actual values at the output, is propagated backwards instead of the original nonlinear function. The new algorithm is tested on the odd/even parity function for orders between 4 and 7 and on high- (180) dimensional data derived from NMR spectroscopy of animal tumours. Results suggest that using the linear function, the network converges faster and is more likely to escape from local minima than when the original BP is used.< >

关键词： backpropagation algorithms Nervous system Testing Nuclear magnetic resonance Spectroscopy Animals Tumors Supervised learning Artificial neural networks Pattern recognition

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A study of crossvalidation and bootstrap as objective functions for genetic algorithms 7

A study of crossvalidation and bootstrap as objective functi...

引用

7th Brazilian Symposium on Neural Networks

作者： de Lacerda, EGM de Carvalho, ACPLF Ludermir, TB Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)0769517099

This article addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic algorithms. This problem is also known as the model selection problem. Some model selection techniques (e.g., cross-validation and bootstrap) are combined with the Genetic algorithms of different ways. Those combinations explore features of the Genetics algorithms such as the ability for handling multiple and noise objective functions.

关键词： Artificial intelligence backpropagation algorithms Character generation Genetic algorithms Humans Informatics Learning systems Machine learning algorithms Neural networks Thumb

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Robust design of artificial neural networks applying the Taguchi methodology and DoE

Robust design of artificial neural networks applying the Tag...

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Electronics, Robotics and Automotive Mechanics Conference

作者： Ortiz-Rodriguez, J. M. Martinez-Blanco, M. R. Vega-Carrillo, H. R. Univ Autonoma Zacatecas Zacatecas 980000 Mexico

ISBN: (纸本)0769525695

The integration of Artificial Neural Networks and optimization provides a tool for designing robust network parameters and improving their performance. The Taguchi method offers considerable benefits in time and accuracy when is compared with the conventional trial and error neural network design approach. This work is concerned with the robust design of multilayer feedforward neural networks trained by backpropagation algorithm and develops a systematic and experimental strategy which emphasizes simultaneous optimization artificial neural network's parameters optimization under various noise conditions. Here, we make a comparison among this method and conventional training methods. The attention is drawing on the advantages on Taguchi methods which offer potential benefits in evaluating the network behavior.

关键词： Taguchi methods Artificial neural networks Multilayer feedforward neural network U. S. Department of Energy backpropagation algorithms female mammal network parameters

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