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Prediction of shape defects over length of cold rotted sheet using artificial neural networks

IRONMAKING & STEELMAKING 2007年第2期34卷 166-176页

作者： Raju, D. V. Iqbal, A. Trivedi, A. Kumar Mukhopadhyay, A. TATE Steel R&D Div Jamshedpur 831001 Bihar India TATE Steel Cold Rolling Mill PLTCM Jamshedpur 831001 Bihar India

A model based on an artificial neural network (ANN) has been developed for prediction of flatness of cold rolled (CR) sheet in a tandem cold rolling mill for white goods applications. Various process parameters including roll bending, roll shifting, tensions between stands etc., which affect flatness of CR sheet are considered in the model. Substantial amounts of data are obtained from level II automation of PL-TCM of TATA Steel to develop the prediction model. The developed ANN model, based on back propagation algorithm, is able to predict the flatness defects like edge buckles, centre buckles for a given set of rolling parameters. The model involves a large number of process parameters and application of ANN to such kind of problems is successfully demonstrated in the present study. The model is in good agreement with the observed flatness values at different locations across the width. High coefficient of determination close to 0.919 is achieved for the prediction of flatness at edges.

关键词： ANN back propagation algorithm cold rolling flatness roll bending roll shifting I units steel statistica

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Neuron curve as a tool for performance evaluation of MLP and RBF architecture in first break picking of seismic data

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JOURNAL OF APPLIED GEOPHYSICS 2014年 108卷 159-166页

作者： Kahrizi, Amin Hashemi, Hosein Islamic Azad Univ Branch Kermanshah Tehran Iran Univ Tehran Inst Geophys Tehran 14174 Iran

Recently wide applications of neural networks are reported in geophysical scientific papers, mostly lack the consideration of their mathematical evaluation and performance. In these general estimators/regression function/classifiers, parameters to be tuned are the number of layers, neurons, type of transfer function, minimum size of training set, etc. These will be carefully tuned per each physical problem. Among all, the number of hidden layers and the number of neurons in each hidden layer are the two important parameters to be decided and normally no rules are available for finding them precisely. In this paper a method to find the hidden layer size is described beside the main purpose of the paper which is to compare the performance of the first break picker networks. We used a known learning-curve and introduce a measure named "neuron-curve" to find the optimal layer size & minimum size of training set. This paper shows the application of these two curves in finding the first break picks of seismic refraction data. Furthermore, the effect of noise on the architecture of two known neural networks (multilayer perceptron and radial basis function) in the first break picking is also investigated. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Artificial neural network back propagation algorithm First break picking Learning curve Neuron curve

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Nonlinear inverse filter using ε-filter and its application to image restoration

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2000年第2期E83A卷 283-290页

作者： Watabe, H Arakawa, K Arakawa, Y Univ Tokyo Adv Sci & Technol Res Ctr Tokyo 1068558 Japan Meiji Univ Dept Comp Sci Kawasaki Kanagawa 2148571 Japan

A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an epsilon-filter, which is modified from the cascaded linear filter. The nonlinear property of the epsilon-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.

关键词： nonlinear inverse filter epsilon-filter back propagation algorithm image restoration

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Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第6期41卷 2660-2677页

作者： Wu, Peitsang Hung, Yung-Yao Lin, Zi-po I Shou Univ Dept Ind Management Kaohsiung 840 Taiwan

Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if-then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Fuzzy neural networks Electromagnetism-like mechanism back propagation algorithm Weights elimination

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New approaches to determine age and gender in image processing techniques using multilayer perceptron neural network

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APPLIED SOFT COMPUTING 2018年 70卷 157-168页

作者： Avuclu, Emre Basciftci, Fatih Aksaray Univ Dept Comp Technol Aksaray Turkey Selcuk Univ Fac Technol Dept Comp Engn Konya Turkey

As a result of some events (disasters, inheritance, disappearances etc.), age and gender determination can be vital for people. Forensic medical institutions make the determination of age by examining the structures such as teeth and bones. Procedures for forensic science are currently estimated manually according to certain morphological findings on the tooth. In this study, 1313 panoramic dental images were used automatically to estimate age. Image preprocessing is applied on these images. Trapezoidal teeth are corrected in the coordinate plane to obtain more accurate and standard results. In the study, the correction process is done with original and novel developed algorithm. Dental images are automatically and dynamically segmented and feature vectors are created by extracting their features. The generated feature vectors are dynamic and presented as inputs to the Multilayer Perceptron Neural Network. Depending on the request, the number of input count reduction process can be performed. In this study, age and gender were determined from dental x-ray images with novel and originally developed algorithm. The application is written in C # programming language. In some tooth groups, the highest classification rate of 100% and age determination with 0 error were performed. With this study, age determination in forensic science will be more accurate. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Age and gender estimation Panoramic dental images Image processing techniques Multi-layer perceptron back propagation algorithm

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Prediction of hot flow stress of CrMoV steel using artificial neural network (ANN)

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ISIJ INTERNATIONAL 2007年第8期47卷 1126-1130页

作者： Bapari, Afshin Najafizadeh, Abbas Moazeny, Mohamamid Isfahan Univ Technol Dept Mat Sci & Engn Esfahan Iran

Hot flow stress of CrMoV steel was extracted using hot compression test. Test was carried out in strain range of 0.1-0.9, with strain rate of 0.1-5 s(-1) and the temperature of 900-1 200 degrees C. Flow stress was predicted by using conventional regression method, i.e., Zenner-Holloman parameter with hyperbolic function. The results showed low accuracy of prediction with a high relative error. Several two hidden layers Artificial Neural Networks (ANN) architectures with back propagation algorithm and momentum learning process were applied using Matlab software. They used strain, strain rate and temperature as input and flow stress as output. It was found that an optimum architecture of 3-9-10-1 shows proper prediction with respect to the conventional regression method, i.e., the relative error reached -0.13 % in place of 11.52 %. This ANN method is also capable of generating high precision output for unseen deformation conditions if proper initial weights and biases are used.

关键词： flow stress prediction CrMoV steel conventional regression method artificial neural network back propagation algorithm

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Artificial Neural Network Modeling for Prediction of Roll Force During Plate Rolling Process

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MATERIALS AND MANUFACTURING PROCESSES 2010年第1-3期25卷 149-153页

作者： Rath, S. Singh, A. P. Bhaskar, U. Krishna, B. Santra, B. K. Rai, D. Neogi, N. SAIL Res & Dev Ctr Iron & Steel Ranchi 834004 Bihar India

Accurate prediction of roll force during hot rolling process is very important for model based automation (Level-2) of plate mills. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. The response of gauge control hardware is highly dependant on the accuracy of prediction of roll force. Traditionally, mathematical models based on plane homogeneous plastic deformation theory are used for prediction of roll force. This method is based on many simplified assumptions which are not valid for actual industrial application. An artificial neural network (ANN)-based data driven model has been developed for prediction of roll force during plate rolling process. A very accurate data acquisition system has been installed in Plate Mill of Bhilai Steel Plant through which input and output parameters have been recorded. For a particular grade of steel, inputs to the ANN model are roll gap of previous pass, roll gap of current pass, rolling temperature, rolling speed, plate width, and pass number (6 inputs). The model output is roll force (1 output). In this article, the methodologies of development, training, and validation of ANN model has been discussed. Feed forward network has been chosen as ANN structure. back propagation algorithm with variable learning rate and conjugate gradient optimization of cost function has been chosen as network training methodology. The model was found to be highly accurate with r-square value about 0.94.

关键词： Artificial neural network back propagation algorithm Plate mill Roll force

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Modular neural network modeling of compressive strength of high-alumina bricks by using tangent function

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MATERIALS & DESIGN 2007年第1期28卷 112-118页

作者： Guler, M. Oguz Artir, Recep Sakarya Univ Fac Engn Dept Met & Mat Engn TR-54187 Adapazari Turkey

In this study, a feed forward modular neural network consists of four layer is presented to predict the compressive strength of the high-alumina bricks. Model hierarchy consists of 12 parameters that affect the production directly. The inputs of the model consist of chemical composition (SiO2%, Al2O3%, TiO2%, Fe2O3%, CaO%, MgO%, Na2O% and K2O%), brick volume, sintering temperature, bulk density and apparent porosity and the output of the model is compressive strength. The transfer function used in the each neural mode was tangent function. Errors of the training feedback to the system by back propagation algorithm (BPA). Because of the limited data range used for training, the prediction results and the level of error 2.78% were satisfactory only within the same range. The ability of the model developed has proven that prediction of the compressive strength of high-alumina bricks established successfully. Sensitivity analysis indicated that bulk density and apparent porosity were the most significant inputs affecting compressive strength within the limits of the model whereas sintering temperature and brick volume were found to be relatively significant in the model. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： high-alumina bricks compressive strength modular neural network tangent function back propagation algorithm

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Financial forecasting using support vector machines

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NEURAL COMPUTING & APPLICATIONS 2001年第2期10卷 184-192页

作者： Cao, L Tay, FEH Natl Univ Singapore Dept Mech & Prod Engn Singapore 117548 Singapore

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the back propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE). Mean Absolute Error (MAE), Directional Symmetry (DS) Correct Up (CP) trend and Correct Down (CD) trend S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial rime series.

关键词： back propagation algorithm financial time series forecasting generalisation multi-layer perceptron support vector machines

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Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 215-230页

作者： S.Muthukumaran P.Geetha E.Ramaraj Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Dr.Umayal Ramanathan College for WomenKaraikudi630003India

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the *** is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world *** extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food *** paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation *** of the researchers used to relay on historical records of meteorological parameters to predict the yield of *** is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy *** real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)algorithm with Genetic algorithm(GA)and Multi Objective Particle Swarm Optimization algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the *** input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the *** database optimized by using Artificial Neural Network back propagation *** reason for improving the growth of paddy was identified using the output of the Neural *** metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed *** analysis made between ANN with GA and ANN with MOPSO to identify the recommenda

关键词： ANN back propagation algorithm genetic algorithm multi objective particle swarm optimization algorithm

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