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2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)

作者： Sarker, G. Natl Inst Technol Comp Sci & Engn Dept Durgapur India

ISBN: (纸本)9781509000357

A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering algorithm (MOCA). Optimal Clustering algorithm (OCA) groups texts into different subject categories. Learning is conducted to extract the attributes along with corresponding weights for each subjects. Fuzzy confusion matrix has been used to measure several performance metrics with Holdout method. These are satisfactory. Over and above the text learning and recognition time is very less making the system efficient also.

关键词： bp algorithm OCA MOCA Keywords Text Attributes Accuracy Precision Recall F-Score Holdout Method Fuzzy Confusion Matrix

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Predictive Study of Forest Fires Based on MIVbp-SVM 29

Predictive Study of Forest Fires Based on MIVBP-SVM

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第29届中国控制与决策会议

作者： Liu Ming Zhang Shuangquan He Yude Liu Ziyi Basic Science College Changchun University of Technology Faculty of Math University of Waterloo

ISBN: (纸本)9781509046584

In this paper, we take the average impact value method as the evaluation of neural network variable correlation indicators, analysis the data provided by Professor *** and A. Morais from University of Minho(Portugal) using the MIVbp algorithm to filtrate 13 characterization factors to get 7 characterization parameters affect forest fires, construct the simulation model of the prediction of forest fires based on the support vector machine algorithm, using a test set for testing, the accuracy rate reached 91.89%.

关键词： MIV algorithm bp algorithm Support Vector Machine Forest Fire

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Grey Wolf Optimizer-based Back-propagation Neural Network algorithm 12

Grey Wolf Optimizer-based Back-propagation Neural Network Al...

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12th International Computer Engineering Conference (ICENCO)

作者： Hassanin, Mohamed F. Shoeb, Abdullah M. Hassanien, Aboul Ella Fayoum Univ Fac Comp & Informat Comp Sci Dept Faiyum Egypt Cairo Univ Fac Comp & Informat IT Dept Giza Egypt

ISBN: (纸本)9781509028634

For many decades, artificial neural network (ANN) proves successful results in thousands of problems in many disciplines. Back-propagation (bp) is one of the candidate algorithms to train ANN. Due to the way of bp to find the solution for the underlying problem, there is an important drawback of it, namely the stuck in local minima rather than the global one. Recent studies introduce meta-heuristic techniques to train ANN. The current work proposes a framework in which grey wolf optimizer (GWO) provides the initial solution to a bp ANN. Five datasets are used to benchmark GWO bp performance with other competitors. The first competitor is an optimized bp ANN based on genetic algorithm. The second is a bp ANN powered by particle swarm optimizer. The third is the bp algorithm itself and lastly a feedforward ANN enhanced by GWO. The carried experiments show that GWObp outperforms the compared algorithms.

关键词： Optimization Grey wolf optimizer bp algorithm Neural Network Training Nature-inspired Aigorithms Meta-heuristic Aigorithms Classification

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A new algorithm for the theoretical energy loss calculation of the distribution network based on artificial neural networks

A new algorithm for the theoretical energy loss calculation ...

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6th International Conference on Machine Learning and Cybernetics

作者： Wang, Hong-Rui Yang, Guo-Jie Liu, Xiu-Ling Hebei Univ Dept Elect & Informat Engn Baoding 071002 Peoples R China

ISBN: (纸本)9781424409723

In this paper, several normal calculation algorithms for the theoretical energy loss of the power distribution network were simply summarized. By analyzing their limitedness in using and applying the technology of artificial neural network (ANN), a novel bp based optimum calculation algorithm for the theoretical energy loss of the power distribution network was proposed. The practical applications showed that the efficiency and veracity of the calculation for theoretical energy loss was improved.

关键词： distribution networks theoretical energy losses artificial neural networks bp algorithm

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Prediction of Arc Voltage of Electric Arc Furnace Based on Improved Back Propagation Neural Network

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SN Computer Science 2021年第3期2卷 167页

作者： Vinayaka, K.U. Puttaswamy, P.S. Siddaganga Institute of Technology Karnataka Tumakuru India GSSSIETW Karnataka Mysore India

The Production of the quality steel is achieved by melting iron and steel scraps by utilizing electric power in an electric arc furnace, it acts as one of the major troublesome load in the electric power system due to high nonlinear and chaotic nature, thereby creating severe power quality disturbances to the interconnected network, therefor the need for model to describe the behavior of electric arc furnace in a simulation of electrical power system become significant, the relation between the arc voltage and arc current is pivotal in defining the characteristics of an electric arc furnace, in this paper, the Voltage and current characteristics based on real time data is discussed, then arc voltage prediction using improved back propagation neural network based on arc current under different zones of operation are simulated using MATLAB/SIMULINK, the results infer the validation of test results with the actual values and effectiveness of prediction by adoption of improved back propagation algorithm and efficient in terms of prediction with reduced errors. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bp algorithm Electric arc furnace Neural network Prediction model

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Reassembling the Fragmented JPEG Images Based on Sequential Pixel Prediction

Reassembling the Fragmented JPEG Images Based on Sequential ...

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1st International Symposium on Computer Network and Multimedia Technology

作者： Xu, Ming Dong, Shule Hangzhou Dianzi Univ Inst Comp Applicat Technol Hangzhou 310018 Peoples R China

ISBN: (纸本)9781424452729

File carving is a digital forensic technique which data files are extracted from unstructured data sources without the information of the previously stored file system. Since JPEG is one of the most popular image formats in the storage and distribution of digital photographic imagery, JPEG Carving aroused numerous researchers' great interest. In this paper, a new algorithm of reassembling the fragmented JPEG file is described. To reassemble the fragmented JPEG file, a sequential pixel prediction model based on the neural network was proposed to determine whether or not the given fragment is adjacent to the last fragment in the original. The performance results obtained from the fragmented test-sets of DFRWS 2006 show that the method can be effectively used in recovery of fragmented JPEG files.

关键词： component data recovery file carving bp algorithm digital forensics

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Compound gradient vector based neural networks for real-time control

Compound gradient vector based neural networks for real-time...

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38th Annual Meeting of the Industry-Applications-Society

作者： Chen, ZP Zhao, H Wei, KX Tianjin Univ Technol Dept Automat Tianjin 300191 Peoples R China

ISBN: (纸本)0780378830

An improved compound gradient vector based a NN online training weight update scheme is proposed in this paper. The convergent analysis indicates that because the compound gradient vector is employed during the weight update, the convergent speed of the presented algorithm is faster than the bp algorithm. In this scheme an adaptive learning factor is introduced, in which the global convergence is obtained. and the convergent procedure on plateau and flat bottom area can speed up. Simulations have been conducted and the results demonstrate that the satisfactory convergent performance and strong robustness are obtained using the improved compound gradient vector NN online learning scheme for real time control.

关键词： neural networks bp algorithm gradient vector Online learning

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Transfomer fault diagnosis based on rough sets theory and Artificial Neural Networks

Transfomer fault diagnosis based on rough sets theory and Ar...

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International Conference on Condition Monitoring and Diagnosis

作者： Yu, Xiaodong Zang, Hongzhi Shandong Inst Light Ind Jinan 250061 Peoples R China Shandong Elect Power Res Inst Jinan 250002 Peoples R China

ISBN: (纸本)9781424416219

Transformer fault diagnosis based on Artificial Neural Networks (ANN) is widely used, because ANN has essential nonlinear character, parallel processing ability and the ability of self organize and self learning. But there exist problems if we use traditional ANN method alone to diagnose transformer fault, the large input vector dimension and complex training database will cause the computation complexity and the space increase greatly, lead to long training time, slow convergence and low judgement accuracy. In this paper, a hybrid fault diagnosis method combining Rough Set (RS) theory and ANN (RS-ANN) is presented. Taking advantage of the strong ability of RS theory in processing large data and eliminating redundant information, this method can remove irrelevant factors from the original data and reduce the amount of training data which helps to overcome ANN's defect when process large database. A number of simulation results show that RS-ANN simplifies the networks' structure, reduces the networks' training epochs, improves the judgement accuracy.

关键词： transformer fault diagnosis rough set artificial neural networks reduction accuracy simulation MATLAB decision table bp algorithm

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Study on a novel method of on-line viscosity measuring based on singlechip

Study on a novel method of on-line viscosity measuring based...

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IEEE International Conference on Mechatronics and Automation

作者： Tong, Gang Tang, Weiyi Liu, Lei Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao Shandong Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China

ISBN: (纸本)9781424408276

The off-line viscosity measuring destroys the real time performance and the precision, and the change of temperature causes a biggish impact to the output of the bridge circuit. This paper proposed an improved method based on singlechip AT89C55WD, which realized the on-line signals gathering and the data processing of many kinds of signals, to measure the fluid viscosity. The configuration of the sensor was given, which was designed independently after many times tests and error analysis, and the bridge circuit was improved again. The temperature measurement was carried out based on the DS18B20-PAR chip, and the temperature compensation was adopted based on the back propagation(bp) neural network algorithm, thus the output signal of the viscosity sensor will be a monodrome function for the survey, the temperature influence was eliminated greatly. Furthermore the multi-sensors Kalman filtering technology and the software fault-tolerant technology as well as the partition non-linearity compensation were applied, thus the cross-interference among the sensors was weakened greatly.

关键词： on-line viscosity measurement singlechip AT89C55WD bp algorithm temperature compensation

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The research with Gabor wavelet neural networks algorithm on target recognition

The research with Gabor wavelet neural networks algorithm on...

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Conference on Electronic Imaging and Multimedia Technology IV

作者： Sun, HG Pan, YX Zhang, JB Chang Chun Univ Sci & Technol Changchun 130022 Peoples R China

ISBN: (纸本)081945592X

The Paper is the study of Gabor wavelet neural network algorithm and its application in gray image target recognition. The mostly thought t are real time recognizing gray image target with Gabor wavelet neural networks algorithm. The main thoughts are through combing the forward neural networks (bp net) with Gabor wavelet based on they were applied in target feature extraction and recognition. A model of Gabor wavelet neural network is constructed with automatic target recognition, the good impact is gained when it is applied target recognition. The principle of Gabor filter is expounded. The multi-channel Gabor filter is designed based on theory and practicality, the neural network recognizing algorithm based on multi-channel Gabor filter feature is presented. Training algorithm of Gabor wavelet neural networks model was given out. Principally analyzed Gabor wavelet neural networks from theory, in the mean time training algorithm of Gabor wavelet network suited to target recognition was designed by bp algorithm. Theory and simulate experiment indicated the astringency and robustness of this algorithm excelled bp net. Target was recognized by this algorithm not only increased recognition precision but also overcame the bug of bp algorithm get in minimum.

关键词： target recognition multi-channel bp algorithm Gabor wavelet neural network

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