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Techniques of Image Processing Based on Artificial neural networks

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Journal of Donghua University(English Edition) 2006年第6期23卷 20-24页

作者：李伟青王群王成彪 School of Engineering and Technology China University of Geosciences School of Information and Technology China University of Geosciences

This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

关键词： neural networks backpropagation networks Chromatism classification edge detection image processing.

Applications of neural networks in Spinning Prediction

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Journal of Donghua University(English Edition) 2003年第1期20卷 58-60页

作者：程文红陆凯 College of Textiles Donghua University Shanghai 200051

The neural network spinning prediction model (BP and RBF networks) trained by data from the mill can predict yarn qualities and spinning performance. The input parameters of die model are as follows: yarn count, diameter, hauteur, bundle strength, spinning draft, spinning speed, traveler number and twist. And the output parameters are: yarn evenness, thin places, tenacity and elongation, ends-down. Predicting results match the testing data well.

关键词： neural networks Spinning prediction

The optimum design of the pressure control spring of the relief valve based on neural networks

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Journal of Coal Science & Engineering(China) 2006年第1期12卷 119-123页

作者：傅晓锦 Department of Mechanical & Electrical Engineering Shaoxing College of Arts and Sciences Shaoxing 312000 China

Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.

关键词： spring neural networks optimal design relief valve

Image-Moment Based Affine Invariant Watermarking Scheme Utilizing neural networks

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Journal of Beijing Institute of Technology 2006年第1期15卷 71-75页

作者：吴健珍谢剑英杨煜普 Department of Automation Shanghai Jiaotong University Shanghai 200030 China

A new image watermarking scheme is proposed to resist rotation, scaling and translation （RST） attacks. Six combined low order image moments are utilized to represent image information on rotation, scaling and translation. Affine transform parameters are registered by feedforward neural networks. Watermark is adaptively embedded in discrete wavelet transform （DWT） domain while watermark extraction is carried out without original image after attacked watermarked image has been synchronized by making inverse transform through parameters learned by neural networks. Experimental results show that the proposed scheme can effectively register affine transform parameters, embed watermark more robustly and resist geometric attacks as well as JPEG2000 compression.

关键词： digital watermark affine transform geometric attacks image moment neural networks

Discovering Frequent Subtrees from XML Data Using neural networks

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 117-121页

作者： SUN Wei LIU Da-xin WANG Tong College of Computer Science and Technology Harbin Engineering University Harbin 150001 Heilongjiang China

By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner （OSTMiner） based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algo- rithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well.

关键词： XML frequent subtrees data mining neural networks

Hybrid H_2/H_∞ force/position control based on neural networks

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Journal of Harbin Institute of Technology(New Series) 2004年第5期11卷 569-572页

作者：温淑焕蔡建羡王洪瑞 Dept. of Electronic Engineering Yanshan University Qinhuangdao 066004China

A neural network control scheme with mixed H2/H∞performance was proposed for robot force/position control under parameter uncertainties and external disturbances. The mixed H2/H∞tracking performance ensures both robust stability under a prescribed attenuation level for external disturbance and H2optimal tracking. The neural network was introduced to adaptively estimate nonlinear uncertainties, improving the system’s performance under parameter uncertainties as well as obtaining the H2/H∞tracking performance. The simulation shows that the control method performs better even when the system is under large modeling uncertainties and external disturbances.

关键词： robotics force/position control mixed H_2/H_∞control neural networks

Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

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Journal of Central South University of Technology 2007年第5期14卷 690-695页

作者：李炜朱新坚莫志军 Department of Automation Fuel Cell Research Institute Shanghai Jiaotong University

A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.

关键词： polymer electrolyte membrane fuel cell(PEMFC) equivalent internal-resistance radial basis function neural networks

Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays

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Science in China(Series F) 2003年第6期46卷 466-474页

作者：王林山徐道义 Department of Mathematics Ocean University of ChinaQingdao 266071China Department of MathematicsLiaocheng UniversityLiaocheng 252059China Mathematical CollegeSichuan University.Chengdu 610064China Mathematical College Sichuan UniversityChengdu 610064China

The authors analyze the existence of the equilibrium point and global exponential stability for Hopfield reaction-diffusion neural networks with time-varying delays by means of the topological degree theory and generalized Halanay inequality. Since the diffusion phenomena and time delay could not be ignored in neural networks and electric circuits, the model presented here is close to the actual systems, and the sufficient conditions on global exponential stability established in this paper, which are easily verifiable, have a wider adaptive range.

关键词： neural networks reaction-diffusion delay stability.

Indian stock market prediction using artificial neural networks on tick data

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Financial Innovation 2019年第1期5卷 267-278页

作者： Dharmaraja Selvamuthu Vineet Kumar Abhishek Mishra Department of Mathematics Indian Institute of Technology DelhiHauz KhasNew Delhi 110016India

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock *** stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic *** description:Support Vector Machines(SVM)and Artificial neural networks(ANN)are widely used for prediction of stock prices and its *** algorithm has its way of learning patterns and then *** neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial *** and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results ***:All three algorithms provide an accuracy of 99.9%using tick *** accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

关键词： neural networks Indian Stock Market Prediction Levenberg-Marquardt Scale Conjugate Gradient Bayesian Regularization Tick by tick data