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检索条件"主题词=Backpropagation Algorithms"

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Recognition Pattern of Arca Siwa Prambanan Temple with Canny and backpropagation Algorithm

Recognition Pattern of Arca Siwa Prambanan Temple with Canny...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Arif Fridasari Ema Utami Bachelor of Informatics Universitas Amikom Yogyakarta Yogyakarta Indonesia Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia

The statue temple of recognition pattern used to distinguish the direction the digital image (photos) in order facilitate tourist or user knows the direction of the capture of temple image. In its application used algorithms the detection of edge Canny and artificial neural network backpropagation and the process of extracting by using the PCA. Canny algorithm applied to the process of image where image processing rgb converted into binary image. While backpropagation algorithms used to recognize patterns of processing that image. The direction of which were recognized by the system which is West, South, Southeast, East, Northeast, and North. The level of accuracy against 36 testing data to the specifications 24 the image of practicing and 12 test image. The level of accuracy testing of the image of trained 100 %, while the image of a test of 83,33 %.

关键词： Image edge detection Neurons Pattern recognition backpropagation Principal component analysis Testing backpropagation algorithms

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A backpropagation learning framework for feedforward neural networks

A backpropagation learning framework for feedforward neural ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： X. Yu M. Onder Efe O. Kaynak Faculty of Informatics and Communication Central Queensland University Rockhampton QLD Australia Department of Electrical and Electronic Engineering Bogazici University Istanbul Turkey

ISBN: (纸本)0780366859

In this paper, a general backpropagation learning framework for the training of feedforward neural networks is proposed. The convergence to global minimum under the framework is investigated using the Lyapunov stability theory. It is shown the existing feedforward neural network training algorithms are special cases of the proposed framework.

关键词： Neural networks Feedforward neural networks Lyapunov method Convergence Informatics backpropagation algorithms Function approximation Predictive models Data mining Neurons

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Optimal distribution of patterns in a heterogeneous array of transputers for backpropagation networks

Optimal distribution of patterns in a heterogeneous array of...

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International Joint Conference on Neural Networks (IJCNN)

作者： Foo Shou King P. Saratchandran N. Sundararajan School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Training set parallelism and network based parallelism are two popular paradigms for parallelising a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This study analyses how we can optimally distribute the training set on a heterogeneous processor network when the number of patterns in the training set is not an integer multiple of the number of processors. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis, it is found that equal distribution of training patterns among a homogeneous array of transputers is not necessarily the optimal way to allocate the patterns to processors even when the training set is an integer multiple of the number of processors.< >

关键词： Intelligent networks backpropagation algorithms Neural networks Network topology Feedforward neural networks Pattern analysis Pipelines Electronic mail Linear programming Parallel machines

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Algorithmic enhancements to a backpropagation interior point learning rule

Algorithmic enhancements to a backpropagation interior point...

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International Conference on Neural Networks

作者： Jun Ji G.V. Meghabghab Department of Mathematics and Computer Science Valdosta State University Valdosta GA USA

In this paper, the authors employ a quadratic interior point method to backpropagation neural networks. The new quadratic backpropagation learning rule searches for a direction which minimizes the objective function in a neighborhood of the current weight vector. Numerical results on the parity problem show that the new learning rule is more than ten times faster than the standard backpropagation, and five times faster than the linear interior point learning rule developed earlier by the same authors (1995).

关键词： backpropagation algorithms Artificial neural networks Neurons Neural networks Multi-layer neural network Computer networks Mathematics Computer science Electronic mail Vectors

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Neural network with lower and upper type-2 fuzzy weights using the backpropagation learning method

Neural network with lower and upper type-2 fuzzy weights usi...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Fernando Gaxiola Patricia Melin Fevrier Valdez Tijuana Institute of Technology Tijuana Mexico

ISBN: (纸本)9781479903467

In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Biological neural networks Neurons Fuzzy logic Time series analysis backpropagation algorithms Learning systems Fuzzy systems

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backpropagation through time for a general class of recurrent network

Backpropagation through time for a general class of recurren...

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International Joint Conference on Neural Networks (IJCNN)

作者： O. De Jeses M.T. Hagan Sch. of Electr. & Comput. Eng. Oklahoma State Univ. Stillwater OK USA School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA School of Electrical and Computer Engineering Oklahoma State University Stillwater OK

This paper introduces a general class of dynamic network, the layered digital dynamic network. It then derives the backpropagation-through-time algorithm for computing the gradient of the network error with respect to... 详细信息

ISBN: (纸本)0780370449

关键词： backpropagation algorithms Computer networks Computer errors Neural networks Output feedback Neurofeedback Computer architecture Feedback loop Perturbation methods Equations

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Choosing among several parallel implementations of the backpropagation algorithm

Choosing among several parallel implementations of the backp...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Petrowski Départment Informatique Institut National de Télécommunications Evry France

Three parallel implementations of the backpropagation algorithm are studied. The performance for each of these implementations is estimated for three prototypes of neural architectures chosen in such a way that they are limit cases of a wide range of feedforward architectures. This study, based on performance analysis validated by experiments, follows two other papers already published.< >

关键词： backpropagation algorithms Neurons Parallel processing Computer networks Neural networks Feedforward systems Iterative algorithms Prototypes Performance analysis Pipelines

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Incorporation of fuzzy classification properties into backpropagation learning algorithm

Incorporation of fuzzy classification properties into backpr...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： M. Sarkar B. Yegnanarayana Department of Computer Science & Engineering Indian Institute of Technology Chennai India

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagation learning algorithm are not tailored to these kinds of classification problems. Hence, in this paper, feedforward neural networks, that use fuzzy objective functions in the backpropagation learning algorithm, are investigated. A learning algorithm is proposed that minimizes an error term, which takes care of fuzziness in classification from the point of view of possibilistic approach. Since the proposed algorithm has possibilistic classification ability, it can encompass different backpropagation learning algorithms based on crisp and constrained fuzzy classification. The efficacy of the proposed scheme is demonstrated on a vowel classification problem.

关键词： backpropagation algorithms Neural networks Feedforward neural networks Fuzzy neural networks Mean square error methods Entropy Computer science Fuzzy sets Marine vehicles

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A modified gradient-based backpropagation training method for neural networks

A modified gradient-based backpropagation training method fo...

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IEEE International Conference on Granular Computing (GRC)

作者： Xuewen Mu Yaling Zhang Department of Applied Mathematics Xidian University Xi'an China Department of Computer Science Xi''an Science and Technology University Xi'an China

A improved gradient-based backpropagation training method is proposed for neural networks in this paper. Based on the Barzilai and Borwein steplength update and some technique of Resilient Propagation method, we adapt the new learning rate to improves the speed and the success rate. Experimental results show that the proposed method has considerably improved convergence speed, and for the chosen test problems, outperforms other well-known training methods.

关键词： Neural networks Artificial neural networks backpropagation algorithms Mathematics Convergence Iterative algorithms Computer science Testing Information processing Biology

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A parallel mapping of backpropagation algorithm for mesh signal processor

A parallel mapping of backpropagation algorithm for mesh sig...

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IEEE Workshop on Neural Networks for Signal Processing

作者： S.A. Khan V.K. Madisetti Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Presents a technique for mapping the backpropagation learning algorithm on a mesh signal processor. The optimal sub-partitioning of computation and communication, and data replication techniques are the key features of the authors' algorithm. Theoretical analysis and simulation results, using the MIT Lincoln Lab simulator, show that the authors' scheme performs better than the other schemes.

关键词： Signal mapping backpropagation algorithms Signal processing Signal processing algorithms Digital signal processing Concurrent computing Data structures Broadcasting Algorithm design and analysis Computational modeling

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