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检索条件"主题词=Backpropagation Algorithms"

共 1893 条记录，以下是341-350 订阅

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A modified gradient-based backpropagation training method for neural networks

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

引用

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 backpropagation algorithm with adaptive learning rate and momentum coefficient

A backpropagation algorithm with adaptive learning rate and ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Chien-Cheng Yu Bin-Da Liu Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

Slower convergence and longer training times are the disadvantages often mentioned when the conventional backpropagation (BP) algorithm are compared with other competing techniques. In addition, in the conventional BP algorithm, the learning rate is fixed and that it is uniform for all the weights in a layer. In this paper, we propose an efficient acceleration technique, the backpropagation with adaptive learning rate and momentum term, which is based on the conventional BP algorithm by employing an adaptive learning rate and momentum factor, where the learning rate and momentum rate are adjusted at each iteration to reduce the training time. Simulation results indicate a superior convergence speed as compared to other competing methods.

关键词： backpropagation algorithms Convergence Acceleration Multilayer perceptrons Pattern classification Image processing Benchmark testing Jacobian matrices Error correction Optimization methods

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Stochastic backpropagation: a learning algorithm for generalization problems

Stochastic backpropagation: a learning algorithm for general...

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： C.V. Ramamoorthy S. Shekhar Div. of Comput. Sci. California Univ. Berkeley CA USA Computer Science Division University of California Berkeley CA USA

Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. The authors discuss the requirements of learning for generalization, which is NP-complete and cannot be approached by traditional methods based on gradient descent. They present a stochastic learning algorithm based on simulated annealing in weight space. The convergence properties and feasibility of the algorithm are verified.< >

关键词： Stochastic processes backpropagation algorithms Neural networks Convergence Testing Noise shaping Speech recognition Computer science Simulated annealing Shape

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An application of a backpropagation network for the control of a tracking behavior

An application of a backpropagation network for the control ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： K. Berns R. Dillmann R. Hofstetter Gruppe Interaktive Planungstechnik Forschungszentrum Infomatik an der Universität Karlsruhe Karlsruhe Germany

The problem of correctly evaluating noisy and incorrect data for the interpretation of ultrasonic sensor signals is addressed. Neural networks, with their inherent characteristics of adaptivity and high fault and noise tolerance, are well suited for such tasks. A backpropagation algorithm is described for the control of the tracking behavior of an autonomous mobile robot. Input data are provided by three ultrasonic sensors mounted on the front of the vehicle. For more flexibility the behavior and learning capability of the tracking algorithm have been improved using different networks.< >

关键词： Vehicles Wheels backpropagation algorithms Mobile robots Ultrasonic variables measurement Sensor phenomena and characterization Neural networks Production facilities Floors Axles

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Improved Image Compression Using backpropagation Networks

Improved Image Compression Using Backpropagation Networks

引用

Workshop on Neural Network Applications and Tools

作者： G. Qiu T.J. Terrell M.R. Varley Department of Electrical and Electronics Engineering University of Central Lancashire UK

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A Constructive Approach of Modified Standard backpropagation Algorithm with Optimum Initialization for Feedforward Neural Networks

A Constructive Approach of Modified Standard Backpropagation...

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International Conference on Computational Intelligence and Multimedia Applications

作者： N. Gunaseeli N. Karthikeyan Department of MCA K.L.N College of Engineering Department of MCA Madurai Kamaraj University India

For the learning process of multilayer feedforward neural network, the constructive approach of Modified backpropagation algorithm (MBP), with optimum initialization is proposed. One of the common complaints about the Standard backpropagation algorithm (SBP) is that it is very slow. Even simple problems may take hundreds of iterations to converge. SBP algorithm reduces only non-linear errors. Much work, therefore has been done in search of faster methods. One of such approach is modified form of the Standard backpropagation algorithm. Modified backpropagation algorithm consists of minimizing the sum of the squares of linear and non- linear errors for all output units. This leads to an efficient process in the network. Proper initialization always plays a key role in the robust neural networks. Therefore, the optimum initialization method is used for weight initialization, which ensures the outputs of neurons are in the active region and the range of activation function is fully utilized. Since the proposed method uses the constructive approach, there is no need to make a prior estimate of the correct network size. The proposed method is implemented on 2 bit parity problem, 4 bit parity checker and encoder problem and produced good results.

关键词： backpropagation algorithms Neural networks Feedforward neural networks Multi-layer neural network Robustness Neurons Convergence Computational intelligence Educational institutions Pediatrics

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Speeding-up backpropagation-a comparison of orthogonal techniques

Speeding-up backpropagation-a comparison of orthogonal techn...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Pfister R. Rojas Fachbereich Mathematik-Institut fur Informatik Freie Universitaet Berlin Berlin Germany

In recent years much effort has been spent trying to develop more efficient variations of the backpropagation learning algorithm. This has led to a combinatorial explosion of learning methods of which no detailed evaluation exists. We have analyzed the most important algorithms and extracted their minimal building blocks. By arranging these building blocks in different forms, and testing the resulting algorithms, we obtained new combinations which were benchmarked in a commercial workstation. Our results show which factors are responsible for the increased speed-up of the tested algorithms. These results could lead to better learning methods for neural networks.

关键词： backpropagation algorithms Multi-layer neural network Neural networks Neurons Learning systems Testing Error correction Decorrelation Acceleration Convergence

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A distributed backpropagation algorithm of neural networks on distributed-memory multiprocessors

A distributed backpropagation algorithm of neural networks o...

引用

Frontiers of Massively Parallel Computation

作者： H. Yoon J.H. Nang S.R. Maeng Center for Artificial Intelligence Research & Department of Computer Science Korea Advanced Institute of Science and Technology Seoul South Korea

A distributed backpropagation algorithm for a fully connected multilayered neural network on a distributed-memory multiprocessor system is presented. The neurons on each layer are partitioned into p disjoint sets, and each set is mapped on a processor of a p-processor system. The algorithm, the communication pattern among the processors, and their time/space complexities are investigated, and the theoretical upper bound on speedup is obtained. The experimental speedup obtained with the algorithm on a ring of 32 transputers, which confirms the model and analysis, is reported. It is found that the choice of processor interconnection topology does not influence the speedup ratio.< >

关键词： backpropagation algorithms Neural networks Neurons Multi-layer neural network Computer simulation Computational modeling Computer networks Concurrent computing Artificial neural networks Algorithm design and analysis

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Implementation with FPGAs of a pipelined on-line backpropagation

Implementation with FPGAs of a pipelined on-line backpropaga...

引用

IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： R.G. Girones A.M. Salcedo Departmento Ing. Electrónica UPV Valencia Spain

The paper describes the implementation of a systolic array for a multilayer perceptron on ALTERA FLEX10KE FPGAs with a hardware-friendly learning algorithm. A pipelined adaptation of the on-line backpropagation algorithm is shown. It better exploits the parallelism because both the forward and backward phases can be performed simultaneously. As a result, a combined systolic array structure is proposed for both phases. Analytic expressions show that the pipelined version is more efficient than the non-pipelined version. The design is implemented and simulated using VHDL at different levels of abstraction and finally mapped on FPGAs.

关键词： Field programmable gate arrays Artificial neural networks Parallel processing backpropagation algorithms Signal processing algorithms Neurons Hardware Systolic arrays Multilayer perceptrons Neural networks

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Fuzzy control of backpropagation

Fuzzy control of backpropagation

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： P. Arabshahi J.J. Choi R.J. Marks T.P. Caudell Department of Electrical Engineering University of Washington Seattle WA USA Boeing Computer Services Inc. Seattle WA USA

The authors propose a fuzzy logic controlled implementation of the backpropagation training algorithm for layered perceptrons. The heuristics for adjusting the value of the learning rate eta are incorporated into a simple fuzzy control system. This provides automatic tuning of the learning rate parameter depending on the shape of the error surface. The application of this straightforward procedure was shown to be able to dramatically improve training time in some problems.< >

关键词： Fuzzy control Fuzzy logic Automatic control backpropagation algorithms Time of arrival estimation Shape

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