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检索条件"主题词=Backpropagation Algorithms"

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Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling

Lattice-based optimization of sequence classification criter...

引用

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

作者： Brian Kingsbury IBM Thomas J. Watson Research Center Yorktown Heights NY USA

Acoustic models used in hidden Markov model/neural-network (HMM/NN) speech recognition systems are usually trained with a frame-based cross-entropy error criterion. In contrast, Gaussian mixture HMM systems are discriminatively trained using sequence-based criteria, such as minimum phone error or maximum mutual information, that are more directly related to speech recognition accuracy. This paper demonstrates that neural-network acoustic models can be trained with sequence classification criteria using exactly the same lattice-based methods that have been developed for Gaussian mixture HMMs, and that using a sequence classification criterion in training leads to considerably better performance. A neural network acoustic model with 153K weights trained on 50 hours of broadcast news has a word error rate of 34.0% on the rt04 English broadcast news test set. When this model is trained with the state-level minimum Bayes risk criterion, the rt04 word error rate is 27.7%.

关键词： Hidden Markov models Neural networks Speech recognition Error analysis Mutual information Broadcasting Maximum likelihood estimation backpropagation algorithms Lattices Acoustic testing

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Dynamic system identification using recurrent neural network with multi-valued connection weight

Dynamic system identification using recurrent neural network...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Arit Thammano Phongthep Ruxpakawong Faculty of Information Technology King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

This paper introduces a new concept of the connection weight to the standard recurrent neural networks - Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, in the modified networks the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against their original counterparts. The results on eleven benchmark problems are very encouraging.

关键词： System identification Recurrent neural networks Neurons Neural networks Feedforward neural networks Nonlinear dynamical systems Neurofeedback backpropagation algorithms Nonlinear systems Information technology

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Evolving Product Unit Neural Networks with Particle Swarm Optimization

Evolving Product Unit Neural Networks with Particle Swarm Op...

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International Conference on Image and Graphics (ICIG)

作者： Rong Huang Shurong Tong School of Management Northwestern Polytechnical University Xi'an China

Product unit neural network (PUNN) training is formulated as an optimization problem and then particle swarm optimization (PSO), an emerging evolutionary computation algorithm, is employed to resolve it. A simple and effective encoding scheme for particles is proposed by which PSO algorithm can configure the architecture and weight of PUNN simultaneously depending on training sets. Because the training algorithm takes into account not only network error but also the complexity of network, the resulting networks alleviate over-fitting. Experimental results show that proposed algorithm achieves rational architecture for PUNN networks and the resulting networks obtain strong generalization abilities.

关键词： Neural networks Particle swarm optimization Neurons Management training Training data Signal processing backpropagation algorithms Genetic algorithms Graphics Computer network management

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Modeling of intelligent calculations in cubic network of workstations

Modeling of intelligent calculations in cubic network of wor...

引用

International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： N. Adam L. Vokorokos B. Mados Technical university of Kosice Slovakia

Article describes model of computation with feedforward multilayer neural network implemented in the network of workstations with cubic topology. Presented computation model is based on parallel error backpropagation algorithm with regard to characteristics of simulations implementation architecture. Effectivity of parallel implementation of feedforward multilayer neural network is represented trough the cost of computation in parallel environment of workstations. Designed computation model allows formally describe components of parallel implementation of neural network. Model provides mathematical instrument for verification of performance, used in simulation of feedforward multilayer neural network on specified hardware platform.

关键词： Intelligent networks Workstations Multi-layer neural network Computational modeling Neural networks Computer networks Feedforward neural networks Concurrent computing Network topology backpropagation algorithms

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Effect of refractoriness on learning performance of a pattern sequence

Effect of refractoriness on learning performance of a patter...

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International Joint Conference on Neural Networks (IJCNN)

作者： Susumu Nagatoishi Osamu Araki

The primary purpose of this study is to reveal the effects of refractoriness on learning performance. We simulated that Elman network, which consists of chaotic neurons, learns a pattern sequence using the back-propagation algorithm. Consequently, the learning speed was accelerated about 46% compared with that of the network consisting of integrate-and-fire model neurons. In addition, we analyzed the required number of hidden neurons, asynchronous activities of hidden neurons' refractoriness. These results suggested that the refractoriness contributes to efficient encoding in the hidden layer of Elman network.

关键词： Neurons Chaos Biological neural networks Acceleration Encoding Neurodynamics Tin Neural networks backpropagation algorithms Helium

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Improving rule extraction from neural networks by modifying hidden layer representations

Improving rule extraction from neural networks by modifying ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Thuan Q. Huynh James A. Reggia Department of Computer Science University of Maryland College Park MD USA

This paper describes a new method for extracting symbolic rules from multilayer feedforward neural networks. Our approach is to encourage backpropagation to learn a sparser representation at the hidden layer and to use the improved representation to extract fewer, easier to understand rules. A new error term defined over the hidden layer is added to the standard sum of squared error so that the total squared distance between hidden activation vectors is increased. We show that this method helps extract fewer rules without decreasing classification accuracy in four publicly available data sets.

关键词： Neural networks Multi-layer neural network Feedforward neural networks Data mining Encoding backpropagation algorithms Supervised learning Matrix decomposition Humans Computer science

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Neuro-computing Method for Data Mining

Neuro-computing Method for Data Mining

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International Conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Jui-Yu Wu Chi-Jie Lu Department of Business Administration Lunghwa University of Science and Technology Taoyuan Taiwan Department of Industrial Engineering and Management Ching Yun University of Technology JungLi Taiwan

Among the advantages of the cerebellar model articulation controller neural network (CMAC NN) include very fast learning, reasonable generalization capability and robust noise resistance, explaining why CMAC NNs are conventionally used in robot control. This study considers the feasibility of CMAC NN as an efficient data mining (DM) method, indicating that the CMAC NN can extend its network topology flexibly to achieve DM applications. Therefore, this study introduces CMAC NN for applying classification and time series prediction problems. The solved problem, network topology, learning algorithm and recommended parameter settings are described as well. Results of this study contribute to efforts to extend network topology for the CMAC NN in DM applications.

关键词： Data mining Neural networks Delta modulation Network topology Clustering algorithms Computational intelligence Artificial neural networks Noise robustness Supervised learning backpropagation algorithms

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Researching about Short-Term Power Load Forecasting Based on Improved BP ANN Algorithm

Researching about Short-Term Power Load Forecasting Based on...

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International Conference on Information Science and Engineering (ICISE)

作者： Yabin Xu Peng Du Inst. of Comput. Beijing Inf. Sci. & Technol. Univ. Beijing China Electron. & Inf. Eng. Inst. Liaoning Ind. Univ. Jinzhou China

First, the forecasting principle and improved algorithms about BP ANN are briefly introduced. Then, an improved algorithm about BP ANN is put forward which based on subordinating degree function, and conduct simulating tests. The result indicates that convergence is rapid without changing the forecasting precision. Based on this and combined with the characteristic of power load forecasting, a model for BP ANN is built, and the corresponding software is designed. A good application effect is achieved to forecast the short-term power load with this software.

关键词： Load forecasting Artificial neural networks Economic forecasting backpropagation algorithms Information science Power engineering and energy Signal processing Convergence Power grids Power system planning

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Application of Artificial Neural Network to Predict Short-Term Capital Flow

Application of Artificial Neural Network to Predict Short-Te...

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International Conference on Research Challenges in Computer Science (ICRCCS)

作者： Xiping Wang Department of Economy and Management North China Electric Power University Baoding China

The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional Back-Propagation algorithm, the Levenberg-Marquardt algorithm was used to train the neural network. The simulation results indicate that the predictive model can be used to carry out the prediction of short-term capital flow.

关键词： Artificial neural networks Predictive models backpropagation algorithms Biological neural networks Biological system modeling Crisis management Neural networks Computer science Application software Computer network management

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Introducing a training methodology for cellular neural networks solving partial differential equations

Introducing a training methodology for cellular neural netwo...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. J. Aein H.A. Talebi Amirkabir University of Technology슠 Tehran Iran Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran

This paper presents an online learning scheme to train a cellular neural network (CNN) which can be used to model multidimensional systems whose dynamics are governed by partial differential equations (PDE). Most of the previous work on CNN, employed fixed parameters or learning methods which need many iterations of an algorithm. There is a lack of fast, online and robust training method in the field of cellular neural networks. The learning method presented in this paper is a modified online backpropagation (BP) algorithm. The modification is concerned with adding a damping term which enhances the robustness of the training scheme. Another modification is decrease the learning rate to avoid unwanted oscillations. To evaluate the performance of the training scheme a set of simulations are performed on two-dimensional heat conduction problem. The results obtained by using CNN are compared to those of analytic solutions.

关键词： Cellular neural networks Partial differential equations backpropagation algorithms Learning systems Signal processing algorithms Robustness Multidimensional systems Neural networks Damping Computational modeling

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