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检索条件"主题词=Back-propagation Algorithm"

共 209 条记录，以下是171-180 订阅

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

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

引用

International Conference on Research Challenges in Computer Science

作者： Wang, Xiping N China Elect Power Univ Dept Econ & Management Baoding 071003 Peoples R China

ISBN: (纸本)9780769539270

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 network short-term capital flow predicting Levenberg-Marquardt algorithm back-propagation algorithm

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A Nonlinear Relay Scheme Resilient to Interference with Unknown CSI 54

A Nonlinear Relay Scheme Resilient to Interference with Unkn...

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54th Asilomar Conference on Signals, Systems and Computers

作者： Wang, Rui Jiang, Yi Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Dept Commun Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9780738131269

This paper studies relay networks under interferences, and proposes a nonlinear amplify-and-forward (NAF) scheme for interference suppression. In contrast to the existing linear amplify-and-forward (AF) relay schemes, the proposed NAF scheme adopts hyperbolic signal amplitude compression for the instantaneous constraint on the transmit power of each antenna of the relay nodes. Noting the striking similarities between the relay network and a three-layer artificial neural network (ANN), we propose a NAF relay scheme inspired by the backpropagation algorithm (NAF-BP) to optimize the weights of the destination and the relay nodes, according to the minimum mean square error (MMSE) criterion. The NAF-BP scheme assumes no channel state information (CSI), no data exchange between the relay nodes, except for a backward channel from the destination to the relays. We also further develop a centralized benchmark algorithm for the NAF relay scheme using the sequential convex programming (SCP), which we refer to as the NAF-SCP. The effectiveness of the proposed scheme is verified through extensive simulations.

关键词： relay schemes interference suppression instantaneous power constraint back-propagation algorithm

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Research of Temperature Controller for Difussion Process Based on Neural Network

Research of Temperature Controller for Difussion Process Bas...

引用

IEEE North-West-Russia-Section Young Researchers in Electrical and Electronic Engineering Conference (ElConRusNW)

作者： Khaing, Zaw Min Anatolii, Schagin Natl Res Univ Elect Technol Dept Automat Control Syst & Control Moscow Russia

ISBN: (纸本)9781509004454

This paper presents the concept of temperature controller for diffusion object based on a back propagation artificial neural network. The control unit consists of two parts, namely, a microcontroller device and a neural network. The control parameters are generated using a neural network. It is used as a standard three-layer neural network with sigmoid function in the input and hidden layers and the linear activation function in the output layer and it is trained by a back-propagation algorithm to achieve an optimal control signal for the diffusion process.

关键词： Diffusion process artificial neural network back-propagation algorithm temperature controller

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Performance Evaluation of Multilayer Feed Forward Neural Network for Handwritten English Vowels Characters

Performance Evaluation of Multilayer Feed Forward Neural Net...

引用

International Conference on Information Systems and Computer Networks (ISCON)

作者： Soni, Rajesh Puja, Durga BSA Coll Engn & Technol Dept Comp Sci Mathura India

ISBN: (纸本)9781467359863;9781467359870

This paper compares the performance of Gradient descent with momentum & adaptive backpropagation (TRAINGDX) and BFGS quasi-Newton backpropagation (TRAINBFG) of backpropagation algorithm in multilayer feed forward Neural Network for Handwritten English Characters of Vowels. This analysis is done with five samples of Handwritten English Characters of Vowels collected from five different people and stored as an image. After partition these scanned image into 4 portions, the densities of these images are determined by using MATLAB function. An input pattern will use these 4 densities of each character as an input for the two different Neural Network architectures. In our proposed work the Multilayer feed forward neural networks will train with two learning algorithms;those are the variant of backpropagation learning algorithm namely Quasi-Newton backpropagation learning algorithm and Gradient descent with momentum and adaptive backpropagation learning algorithm for training set of the Handwritten English Characters of Vowels. The performance analysis of both Neural Network architectures is done for convergence and nonconvergence. Different observations have been considered for trends of error in the case of nonconvergence. From the observation of the result, it can be shown that in the performance of these above two learning algorithms with the training set of handwritten characters of Vowels, there is limitation of gradient descent learning algorithm for convergence due to the problem of local minima which is inherit problem of backpropagation learning algorithm.

关键词： Neural Network Character Recognition back-propagation algorithm Multilayer Feed Forward Neural Network Gradient Descent with Momentum & Adaptive backpropagation BFGS Quasi-Newton backpropagation

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Projection-Based Features: A Superior Domain for Handwritten Bangla Basic Characters Recognition 9

Projection-Based Features: A Superior Domain for Handwritten...

引用

IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

作者： Kabir, Kazi Lutful Shafin, Md. Kishwar Anannya, Tasmiah Tamzid Debnath, Deepon Kabir, Md. Rayhan Islam, Md. Aminul Sarwar, Hasan Mil Inst Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh United Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781479964802

In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces the usage of projection profile features in recognizing handwritten Bangla basic characters. Alongside it also demonstrates a qualitative and quantitative analysis to visualize the effect of using projection based features on accuracy of recognition of Bangla handwritten characters through a number of approaches. In fact, this particular effort comprises of five different approaches where first one used longest-run, quad-tree and octant centroid features, second one adopted additional shadow features in association with the features of first approach, third one used longest run, quad-tree, shadow and chain code histogram features, next approach used longest-run, quadratic center of mass, shadow and left projection profile features and finally fifth approach with additional right projection profile features along with other features involved in the fourth approach. Throughout this analysis, neural network (trained via back-propagation algorithm) acted as classifier to observe the change in accuracy of recognition. It is seen that, with the increase in number of projection-based features, percentage of accuracy enhances at a greater rate than in case of inclusion of other features. This effective analysis can certainly assist a researcher to choose the optimal feature vector (consisting of several feature sets) for handwritten Bangla basic characters recognition.

关键词： Accuracy artificial neural network back-propagation algorithm handwritten character recognition projection-based features

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Optimisation of the range of an optical fibre pH sensor using feed-forward artificial neural network

Optimisation of the range of an optical fibre pH sensor usin...

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6th European Conference on Optical Chemical Sensors and Biosensors (Europt(r)ode VI)

作者： Suah, FBM Ahmad, M Taib, MN Univ Kebangsaan Malaysia Sch Chem Sci & Food Technol Fac Sci & Technol Bangi DE Malaysia Univ Teknol MARA Fac Elect Engn Selangor DE Malaysia

A broad range of optical fibre pH sensor based on immobilised bromophenol blue (BPB) immobilised on hydrophobic organic polymers Amberlite XAD 7 is presented in this paper. The reflectance spectra of the immobilised bromophenol blue were measured by using an optical fibre spectrophotometer. A back-propagation (BP) artificial neural network (ANN) model was used to analyse the optode response. The results showed that the use of ANN technique was very effective in broadening the limited dynamic response of the pH sensor (pH 2.00-5.00) to an extensive calibration response (pH 2.00-12.00). A network with 11 neurons in the hidden layer was tremendously accurate in predicting the response of the optical fibre pH sensor with an average error 0.02 pH for measuring unidentified buffer solution. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： optical fibre pH sensor artificial neural network back-propagation algorithm bromophenol blue amberlite XAD 7

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NeuDetect: A Neural Network Data Mining Wireless Network Intrusion Detection System 10

NeuDetect: A Neural Network Data Mining Wireless Network Int...

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14th International Database Engineering and Applications Symposium (IDEAS)

作者： Ezeife, C. I. Rahman, Md. Zillur Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781605589008

This paper proposes NeuDetect, which applies a classification rule mining Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets. To address the problem of high false alarm rate confronted by current wireless intrusion detection systems, this paper presents a method of applying artificial neural networks mining classification technique to wireless network intrusion detection system. The proposed system, NeuDetect, solution approach is to find normal and anomalous patterns on pre-processed wireless packet records by comparing them with training data using back-propagation algorithm.

关键词： hardware sensor wireless attacks Wi-Fi neural network back-propagation algorithm training data

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Arabic and Latin writing discrimination system

Arabic and Latin writing discrimination system

引用

International Conference on Computer Science and Its Applications, CIIA 2006

作者： Boumaza, Kamel Sellami, Mokhtar Badji Mokhtar Annaba University Research Laboratory on Computer Sciences Algeria

In this paper, we propose a method of an Arabic and Latin discrimination writings, based on morphological, geometrical and statistical analysis. We focused on feature extractions in order to curry out a system able to differentiate between printed or handwritten Arabic and Latin scripts. First, an image acquisition is made by means of a scanner, and then come some pre-treatment steps. After that, we extract the different features constituting an input vector of multi-layer perceptron (MLP) with back-propagation algorithm. Finally and thanks to the network outputs we have been able to undertake a discrimination of each script (Printed or handwritten). The aim of our work is to constitute a postal sorting system in French-Arabic speaking countries.

关键词： Arabic and Latin writing discrimination back-propagation algorithm Geometrical MLP Morphological Postal sorting system Printed and handwritten scripts Statistical features

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A PID neural network controller

A PID neural network controller

引用

International Joint Conference on Neural Networks

作者： Yu, YQ Huang, Y Zeng, B Guangzhou Univ Technol Inst Comp Sci & Intelligent Engn Guangzhou 510080 Peoples R China

ISBN: (纸本)0780378989

In this paper, the new fuzzy PID controller, which is combined fuzzy controller with PID neural network (PIDNN), is proposed. Its structure is difference from the normal one. The feature of it is to use a PIDNN replace PID parameter loop in controller. And the controller is optimized by the learning processing of PIDNN. The principle of PIDNN is discussed and. the learning method based on back-propagation-algorithm is given. The two processes, first and second order systems, are simulated. Results of simulating show that the fuzzy PID controller presented in this paper is a better adaptive controller for linear or nonlinear plant.

关键词： fuzzy PID controller PID neural networks back-propagation algorithm

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Matlab Implementation Of Multilayer Perceptron For Bearing Faults Classification

Matlab Implementation Of Multilayer Perceptron For Bearing F...

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作者： Doseděl, Martin Brno University of Technology

This paper deals with implementation of multilayer perceptron neural network (NN) forbearing faults classification. Neural network has been created from scratch as an M-script with backpropagation learning algorithm also, but without using advanced MATLAB packages. Public availablebearing dataset from CaseWestern Reserve University has been used for both training and testingphase, as well as for the final classification process. Problem with sparse input data for training thenetwork has also been addressed. This relatively simple and small neural network is capable to classifythe failures of a bearing with very low error rate.

关键词： Multilayer perceptron (MLP) deep learning data classification back-propagation algorithm bearing faults

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