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检索条件"主题词=Backpropagation Algorithms"

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backpropagation algorithm for multiresolution image classification

Backpropagation algorithm for multiresolution image classifi...

引用

IEEE International Conference on Image Processing

作者： H. Osman S.D. Blostein Department of Electrical and Computer Engineering Queen's University Kingston ONT Canada

This paper proposes a variation of the standard backpropagation (BP) algorithm that is particularly suitable for training neural networks utilized in multiresolution image classification. The aim is to solve the problem of determining which image resolutions to utilize as network inputs. The approach is to have a mix of image resolutions as network inputs and then apply the proposed BP variant. This variant not only solves the image classification problem, but also removes redundant multiresolution network inputs aiming at attaining only those necessary for classification. Removal of redundant inputs is based on that during the backward phase of BP training the network input units whose inputs are extracted from shared image space inhibit one another. Experimental results that demonstrate the viability of the proposed BP variant are presented.

关键词： backpropagation algorithms Image resolution Image classification Neural networks Cost function Pixel

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Fast backpropagation learning using steep activation functions and automatic weight reinitialization

Fast backpropagation learning using steep activation functio...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： T.-H. Cho R.W. Conners P.A. Araman The Spatial Data Analysis Laboratory Virginia Polytechnic Institute and State University Blacksburg VA USA

Several backpropagation (BP) learning speed-up algorithms that employ the gain parameter, i.e., steepness of the activation function, are examined to determine the effect of increased gain on learning time. It was shown by simulation that although these algorithms can converge faster than the standard BP learning algorithm on some problems, they can be unstable in convergence, i.e., they frequently fail to converge within a finite time. One main reason for this divergence is inappropriate setting of initial weights in the network. To overcome this instability, an automatic random reinitialization of the weights is proposed when convergence speed becomes very slow. BP learning algorithms with this weight reinitialization and larger initial gain (around 2 or 3) were found to be much faster and more stable in convergence than those without weight reinitialization.< >

关键词： Convergence Performance gain Multi-layer neural network Neural networks Data analysis Laboratories backpropagation algorithms Feedforward neural networks Artificial neural networks Neurons

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Neural training of complex sequential associations using recurrent continuous backpropagation

Neural training of complex sequential associations using rec...

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International Conference on Neural Networks

作者： L.H. Chen P.B. Tan M.K. Weir S.K. Foo School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

Proposes a path-based neural network algorithm called recurrent continuous backpropagation two (RCBP-2) for complex sequential processing using a gradient descent method. Under the path-based approach, the goal weights are a collection of weight states. Coupled with the underlying continuity of training exemplars and sequential nature of the system attributes, RCBP-2 can achieve arbitrarily close approximations of complex trajectories within a fixed and relatively small network topology. The performance of RCBP-2 is also monitored by training and subsequently testing on a 4-orbits problem. The results show that RCBP-2 results in a fast and efficient algorithm for complex sequential processing.

关键词： Neural networks Recurrent neural networks backpropagation algorithms Network topology Monitoring Testing

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Evaluation of backpropagation Reconstruction Algorithm for 3D Microwave Imaging of Body Part Anomalies

Evaluation of Backpropagation Reconstruction Algorithm for 3...

引用

IEEE International Symposium on Antennas and Propagation

作者： Claire Migliaccio Christian Pichot Ian Platt Adrian Tan Ian Woodhead Universite Cote d'Azur CNRS LEAT Sophia Antipolis France Lincoln Agritech Ltd Lincoln Canterbury New Zealand

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166704;9781728166711

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

关键词： Microwave measurement Microwave antennas Three-dimensional displays backpropagation algorithms Reconstruction algorithms Image reconstruction Microwave imaging

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Adaptive equalization using the complex backpropagation algorithm

Adaptive equalization using the complex backpropagation algo...

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International Conference on Neural Networks

作者： Cheolwoo You Daesik Hong Department of Electronic Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780332105

For decreasing intersymbol interference (ISI) due to band-limited channels in digital communication, the uses of equalization techniques are necessary. Among adaptive equalization techniques, because of their ease of implementation and nonlinear capabilities, the neural networks have been used as an alternative for effectively dealing with the channel distortion, especially the nonlinear distortion. The complex backpropagation (BP) neural networks are proposed as nonlinear adaptive equalizers that can deal with both QAM and PSK signals of any constellation size (e.g. 32-QAM, 64-QAM and MPSK), and the complex BP algorithm for the new node activation functions having multi-output values and multi-saturation regions is presented. We also show that the proposed complex BPN provides, compared with the linear equalizer using the least mean squares (LMS) algorithm, an interesting improvement concerning bit error rate (BER) when channel distortions are nonlinear.

关键词： Adaptive equalizers backpropagation algorithms Nonlinear distortion Intersymbol interference Neural networks Bit error rate Digital communication Quadrature amplitude modulation Phase shift keying Constellation diagram

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An Application of backpropagation Neural Network for Sales Forecasting Rice Miling Unit

An Application of Backpropagation Neural Network for Sales F...

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International Conference of Computer Science and Information Technology

作者： Mendarissan Aritonang Denny Jean Cross Sihombing Informatics Management Department Methodist University of Indonesia Medan Indonesia Information System Department Atma Jaya Catholic University of Indonesia Jakarta Indonesia

ISBN: (数字)9781728154237

ISBN: (纸本)9781728154244

There are many rice fields and rice milling factories. Rice Milling Units (RMU) are still many who have not applied the prediction method for the sale of rice so that it can affect the availability of raw materials. The purpose of this research is to forecast product sales (rice) at RMU so that it can know the number of raw materials needed so that they avoid idle time. Obtain optimal forecasting; it compared to 2 (two) forecasting methods, Linear Regression, and Artificial Neural network with a backpropagation algorithm. The results showed that the value of MSE on a linear regression method of 0.214, while at the time using Artificial Neural Network obtained an MSE value of 0.00099713. Based on the value of the MSE, the smallest MSE is forecasting by the backpropagation Neural Network method.

关键词： backpropagation Neural Network Linear Regression Sales Forecasting Artificial Inteligence Linear Regression Mean square error Sales back propagation Prediction methods Rice mill grain backpropagation algorithms Artificial neural networks raw material Neural network

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Adaptive error-constrained backpropagation algorithm

Adaptive error-constrained backpropagation algorithm

引用

IEEE Workshop on Neural Networks for Signal Processing

作者： Sooyong Choi KyunByoung Ko Daesik Hong Department of Electrical and Computer Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780371968

In order to accelerate the convergence speed of the conventional BP algorithm, constrained optimization techniques are applied to the BP algorithm. First, the noise-constrained least mean square algorithm and the zero noise-constrained LMS algorithm are applied (designated the NCBP and ZNCBP algorithms, respectively). These methods involve an important assumption: the filter or the receiver in the NCBP algorithm must know the noise variance. By means of extention and generalization of these algorithms, the authors derive an adaptive error-constrained BP algorithm and its simplified algorithm, in which the error variance is estimated. This is achieved by modifying the error function of the conventional BP algorithm using Lagrangian multipliers. The convergence speeds of the proposed algorithms are 20 to 30 times faster than those of the conventional BP algorithm, and are faster than or almost the same as that achieved with a conventional linear adaptive filter using an LMS algorithm.

关键词： backpropagation algorithms Signal processing algorithms Neural networks Convergence Acceleration Lagrangian functions Least squares approximation Filters Additive noise Gaussian noise

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Prediction of Students' GPA Using Levenberg–Marquardt backpropagation Algorithm

Prediction of Students' GPA Using Levenberg–Marquardt Backp...

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International Conference for Convergence of Technology (I2CT)

作者： Igor Didier Sabukunze Yohanes Alvinika Billy Josef Waworuntu Paulus Mudjihartono Universitas Atma Jaya Yogyakarta Yogyakarta Indonesia

Courses marks are the main factors that contribute to the Grade Point Average, commonly called GPA. Besides, GPA describes the academic success of the student. Predicting the students' GPA becomes essential for the student in preparing for their studies in the next semester. However, the prediction itself needs to be reviewed by students to get the proper weighted value. The purpose of this research is to predict the students' GPA as an information exchange study using the Levenberg-Marquardt backpropagation algorithm. This study applies to the Higher Secondary Certificate Students' GPA. The required dataset is downloaded from the Kaggle website. The results show that the best architecture for solving the problem is a 9-12-1 pattern. It implies that there are nine units in the input layer, 12 neurons in the hidden layer, and one unit in the output layer which results are in a high predictive level.

关键词： backpropagation Neurons backpropagation algorithms Manuals Information exchange Biological neural networks Convergence

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Opposite Transfer Functions and backpropagation Through Time

Opposite Transfer Functions and Backpropagation Through Time

引用

IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

关键词： Transfer functions backpropagation algorithms Signal processing algorithms Recurrent neural networks Acceleration Convergence Computational intelligence Stability Nonlinear dynamical systems Computational complexity

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CAD modeling of complex resonant frequencies of a rectangular microstrip patch with a superstrate using complex backpropagation algorithm

CAD modeling of complex resonant frequencies of a rectangula...

引用

IEEE Applied Electromagnetics Conference (AEMC)

作者： Dhruba C. Panda Prabhat K. Patnaik Department of Electronic Science Berhampur University Odisha India Dept. of ECE S.O.E.T Centurion University of Technology and Management Paralakhemundi Odisha India

This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoreti... 详细信息

关键词： Resonant frequency Biological neural networks backpropagation backpropagation algorithms Neurons Microstrip antennas Feeds

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