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检索条件"主题词=Backpropagation Algorithms"

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The realization of quantum complex-valued backpropagation neural network in pattern recognition problem

The realization of quantum complex-valued backpropagation ne...

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International Conference on Neural Information Processing

作者： J.L. Mitrpanont A. Srisuphab Department of Computer Science Mahidol University Bangkok Thailand

The paper presents the approach of the quantum complex-valued backpropagation neural network or QCBPN. The challenge of our research is the expected results from the development of the quantum neural network using complex-valued backpropagation learning algorithm to solve classification problems. The concept of QCBPN emerged from the quantum circuit neural network research and the complex-valued backpropagation algorithm. We found that complex value and the quantum states share some natural representation suitable for the parallel computation. The quantum circuit neural network provides a qubit-like neuron model based on quantum mechanics with quantum backpropagation-learning rule, while the complex-valued backpropagation algorithm modifies standard backpropagation algorithm to learn complex number pattern in a natural way. The quantum complex-valued neuron model and the QCBPN learning algorithm are described. Finally, the realization of the QCBPN is exploited with a simple pattern recognition problem.

关键词： Neural networks Intelligent networks Pattern recognition Quantum computing backpropagation algorithms Neurons Quantum mechanics Computer networks Circuits Concurrent computing

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backpropagation for linearly-separable patterns: A detailed analysis

Backpropagation for linearly-separable patterns: A detailed ...

引用

International Conference on Neural Networks

作者： P. Frasconi M. Gori A. Tesi Dipartimento di Sistemi e Inf. Florence Univ. Italy Florence Italy

A sufficient condition for learning without local minima in multilayered networks is proposed. A fundamental assumption on the network architecture is removed. It is proved that the conclusions drawn by M. Gori and A. Tesi (IEEE Trans. Pattern Anal. Mach. Intell., vol.14, no.1, pp.76-86, (1992)) also hold provided that the weight matrix associated with the hidden and output layer is pyramidal and has full rank. The analysis is carried out by using least mean squares (LMS)-threshold cost functions, which allow the identification of spurious and structural local minima.< >

关键词： Pattern analysis Cost function Sufficient conditions Neurons Electronic mail backpropagation algorithms Interpolation Algorithm design and analysis Shape Joining processes

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Lyapunov stability-based adaptive backpropagation for discrete time system

Lyapunov stability-based adaptive backpropagation for discre...

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International Symposium on Signal Processing and Its Applications (ISSPA)

作者： Z. Man S.K. Phooi H.R. Wu School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering Monash University Clayton VIC Australia

Lyapunov stability-based adaptive backpropagation (LABP) for discrete systems is proposed in this paper. It can be applied to various aspects of adaptive signal processing. A Lyapunov function of the error between the desired and actual outputs of the neural network is first defined. Then the error is backward-propagated based on Lyapunov stability theory so that it can be used to adaptively adjust the weights of the inner layers of the neural networks. Subsequently, this will lead to an error between the desired and actual outputs converging to zero asymptotically. The proposed scheme possesses distinct advantages over the conventional BP by assuring that the system will not get stuck in local minima. Furthermore, this scheme has a faster convergence property and the stability is guaranteed by Lyapunov stability theory. A simulation example is performed to support the proposed scheme.

关键词： Stability Discrete time systems Signal processing algorithms Neural networks Lyapunov method Adaptive signal processing Iterative algorithms backpropagation algorithms Australia Convergence

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Feature windowing-based Thai text-dependent speaker identification using MLP with backpropagation algorithm

Feature windowing-based Thai text-dependent speaker identifi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sutat Sae-Tang C. Tanprasert National Science and Technology Development Agency National Electronics and Computer Technology Center Software and LanguagJ Engineering Laboratory Ministry of Science Technology and Environment Bangkok Thailand

This paper presents the development of Thai text dependent speaker identification system by applying two feature-feeding approaches. A well-known multilayer perceptron (MLP) network with backpropagation learning algorithm is chosen. It has fast processing time and good performance for pattern recognition problems. But MLP has a limitation in that a network must have a fixed amount of input nodes. Therefore, the linear interpolation time normalization is chosen to adjust the input speech signal into a fixed size of input vector. Furthermore, the windowing technique is developed to avoid the distortion caused by a time normalization process. A fixed size window is sliced through the preprocessed features with fixed amount of overlapping frames. The high identification rate observed in experiments confirms that the developed windowing is suitable for the proposed Thai text-dependent speaker identification system.

关键词： Speaker recognition backpropagation algorithms Multilayer perceptrons Natural languages Feature extraction Speech recognition Engines Linear predictive coding Cepstral analysis Frequency

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Implementation of the backpropagation Algorithm for Prognosis of the Number of New Students

Implementation of the Backpropagation Algorithm for Prognosi...

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Computer Science and Engineering (IC2SE), International Conference on

作者： Welnof Satria Sabrina Aulia Rahmah Faculty of Computer Engineering and Science Universitas Dharmawangsa Medan Indonesia

ISBN: (纸本)9781665400473

The number of new students in the Faculty of Computer Engineering and Science of Universitas Dharmawangsa is fluctuating. As the number of new students fluctuates, the university should anticipate any potential weaknesses using the prognosis model to forecast the number of prospective students. An artificial neural network system with backpropagation method applied in this study was expected to solve problems in the prognosis of the number of new students. This study aimed to develop a system capable of forecasting the number of prospective students through a backpropagation algorithm of an artificial neural network program. This study employed new students from three departments in the last five years (2016-2020). The results of this study were 78 students consisting of 21 students from the Information Systems D3 Study Program, 36 students from the Information Technology Bachelor Degree, and 19 students from the Software Engineering Bachelor Degree, this study developed a web-based application capable of forecasting the number of prospective students that are likely to attend their undergraduate degree in the Faculty of Computer Engineering and Science of Universitas Dharmawangsa. The result of this study could be used as a consideration regarding the strategies for increasing the number of prospective students.

关键词： backpropagation Training data Artificial neural networks backpropagation algorithms Predictive models Prognostics and health management Information technology

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Speeding up the convergence of backpropagation networks

Speeding up the convergence of backpropagation networks

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： S. Sureerattanan Huynh Ngoc Phien Asian Institute of Technology Thailand

A new algorithm is proposed for speeding up the convergence of backpropagation (BP) networks. This algorithm is obtained by applying the momentum term and adaptive neuron model with temperature momentum term to the Kalman filter (KF) algorithm. It is found that this algorithm can perform satisfactorily in all cases considered. Not only the convergence rate can be improved, but also the sum of squared errors can be further reduced.

关键词： Convergence Neurons Temperature backpropagation algorithms Multilayer perceptrons Multi-layer neural network Equations Electronic mail Nonhomogeneous media Supervised learning

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Recognition of student emotion based on matrix-1 median fisher's face and backpropagation algorithm

Recognition of student emotion based on matrix-1 median fish...

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International Conference on Electrical Engineering and Informatics (ICELTICs)

作者： Slamet Wibawanto Kartika Candra Kirana Electrical Engineering Universitas Negeri Malang Malang Indonesia Informatics Engineering Universitas Negeri Malang Malang Indonesia

Emotions drive learning success because they hold a willingness to process information. However, it is a challenge for understanding the emotions of student in the real class. In this study, we proposed recognition of student emotion using matrix-1 median fisher's face and backpropagation algorithm. The computation of backpropagation is influenced by neuron architecture which is can be handled by feature reducing, such as fisher face. However the number of fisher's vector due to the number of class. In order to map the lower dimensional feature space than fisher's face vector, we proposed matrix-1 median of the fisher's face. In this proposed method, after face is detected, LDA on PCA space is employed for getting the fisher's face. Then fisher face is transformed into fisher's median. The backpropagation algorithm is trained using this feature to distinguish student emotions. The performances of proposed algorithm are evaluated on the UM's learning video using accuracy and iteration consuming. Our proposed method reach accuration of overly interest, interest and bored up to 0.83, 0.91, and 1, whereas original fisher face reach accuration of overly interest, interest and bored up to 0.83, 0.91, and 0.91. Combination of backpropagation and matrix-1 median fisher face need 9 iteration for training. Whereas the combination of backpropagation and fisher Face need 11 iteration. Experiment result shows that our proposed method outperform than the existing method.

关键词： Face Mathematical model Neurons Principal component analysis Emotion recognition backpropagation backpropagation algorithms

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RRANN: a hardware implementation of the backpropagation algorithm using reconfigurable FPGAs

RRANN: a hardware implementation of the backpropagation algo...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.G. Eldredge B.L. Hutchings Department of Electrical and Computer Engineering Brigham Young University Provo UT USA

Field programmable gate arrays (FPGAs) are an excellent technology for implementing neural networking hardware. This paper presents the run-time reconfiguration artificial neural network (RRANN). RRANN is a hardware implementation of the backpropagation algorithm that is extremely scalable and makes efficient use of FPGA resources. One key feature is RRANN's ability to exploit parallelism in all stages of the backpropagation algorithm including the stage where errors are propagated backward through the network. This architecture has been designed and implemented on Xilinx XC3090 FPGAs, and its performance has been measured.< >

关键词： Hardware backpropagation algorithms Field programmable gate arrays Neurons Neural networks Application software Artificial neural networks Runtime Feeds Speech

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Gain Parameters Based Complex-Valued backpropagation Algorithm for Learning and Recognizing Hand Gestures

Gain Parameters Based Complex-Valued BackPropagation Algorit...

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International Joint Conference on Neural Networks

作者： Yuanshan Liu He Huang Tingwen Huang School of Electronics and Information Engineering Soochow University Texas A&M University at Qatar

ISBN: (纸本)9781479914821

In this paper, an improved complex-valued backpropagation algorithm with gain parameters is proposed. It is then employed to train a complex-valued feedforward neural network with one hidden layer. The well-trained complex-valued neural network is finally applied to deal with the recognition problem of 26 hand gestures. The results of experiment clearly show that much better performance can be achieved by our improved complex-valued backpropagation algorithm than some existing methods.

关键词： Biological neural networks Neurons backpropagation Convergence Joints backpropagation algorithms

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Fluctuation analysis of a two-layer backpropagation algorithm used for modelling nonlinear memoryless channels

Fluctuation analysis of a two-layer backpropagation algorith...

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Asilomar Conference on Signals, Systems & Computers

作者： N.J. Bershad M. Ibnkahla G. Blauwens J. Cools A. Soubrane N. Ponson Department of Electrical and Computer Engineering University of California Irvine CA USA National Polytechnics Institute of Toulouse ENSEEIHT GAPSE Toulouse France Department of Electrotechnology-ESAT Catholic University of Louvain Belgium

Neural networks have previously been used for modelling the nonlinear characteristics of memoryless nonlinear channels using the backpropagation learning (BP) with experimental training data (Ibnkahla et al. 1997). The mean transient and convergence behavior of a simplified two-layer neural network have also been studied (Bershad et al. 1997). The network was trained with zero mean Gaussian data. This paper extends these results to include the effects of the weight fluctuations upon the mean-square-error (MSE). A new methodology is presented which can be extended to other nonlinear learning problems. The new mathematical model is able to predict the MSE learning behavior as a function of the algorithm step size /spl mu/. Linear recursions are derived for the variance and covariance of the weights which depend nonlinearly upon the mean weights. As in linear gradient search problems (LMS, etc.), there exists an optimum /spl mu/ (minimizing the MSE) which is the trade-off between fast learning and small weight fluctuations. Monte Carlo simulations display excellent agreement with the theoretical predictions for various /spl mu/.

关键词： Fluctuations Algorithm design and analysis backpropagation algorithms Neural networks Training data Convergence Mathematical model Search problems Least squares approximation Displays

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