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Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised neural Network

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Computers, Materials & Continua 2023年第2期74卷 2415-2430页

作者： Narongsak Yotha Qusain Hiader Zulqurnain Sabir Muhammad Asif Zahoor Raja Salem Ben Said Qasem Al-Mdallal Thongchai Botmart Wajaree Weera Department of Applied Mathematics and Statistics Rajamangala University of Technology IsanNakhon Ratchasima30000Thailand Department of Mathematics University of GujratGujrat50700Pakistan Department of Mathematics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Department of Mathematical Science College of ScienceUnited Arab Emirates UniversityAl AinAbu DhabiUAE Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

This study aims to solve the nonlinear fractional-order mathematical model(FOMM)by using the normal and dysregulated bone remodeling of themyeloma bone disease(MBD).For themore precise performance of the model,fractional-order derivatives have been used to solve the disease model *** FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts(OC)and bone formation or osteoblasts(OB).The connections of OC and OB are represented by a nonlinear differential system based on the cellular components,which depict stable fluctuation in the usual bone case and unstable fluctuation through the *** myeloma causes by increasing the OC and reducing the osteoblasts,resulting in net bone waste the tumor *** solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation(LVMBP)neural networks(NN),i.e.,*** mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the *** static structural performances are 82%for investigation and 9%for both learning and *** performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton *** accomplish the capability,steadiness,accuracy,and ability of the LVMBPNN,the performances of the error histograms(EHs),mean square error(MSE),recurrence,and state transitions(STs)will be provided.

关键词： Bone remodeling fractional-order myeloma disease artificial neural networks levenberg-marquardt backpropagation population cell dynamics

Yarn Properties Prediction Based on Machine Learning Method

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Journal of Donghua University(English Edition) 2007年第6期24卷 781-786页

作者：杨建国吕志军李蓓智 College of Mechanical Engineering Donghua University

Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so *** vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization *** study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn *** selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research *** results have been compared with those of artificial neural network(ANN)*** investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.

关键词： machine learning support vector machines artificial neural networks structure risk minimization yarn quality prediction

Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

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能源与人工智能(英文)

能源与人工智能(英文) 2022年第4期10卷 60-72页

作者： Elias Galiounas Tom G.Tranter Rhodri E.Owen James B.Robinson Paul R.Shearing Dan J.L.Brett Electrochemical Innovation Lab Department of Chemical EngineeringUniversity College LondonLondonWC1E 7JEUK The Faraday Institution Quad OneBecquerel AvenueHarwell CampusDidcotOX11 The Alan Turing Institute British Library96 Euston RoadLondon NW12DB

The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression *** approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak *** applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform *** alone can be exploited for SoC *** is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the *** later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not *** waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical *** is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation *** importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage *** makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is *** is obtained experimentally from a 210 mAh LiCoO2/graphite pouch *** estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.

关键词： Battery diagnostics Ultrasonic battery monitoring Acoustic battery inspection Mechanical-electrochemical correlation Machine learning artificial neural networks

Research status and applications of nature-inspired algorithms for agri-food production

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International Journal of Agricultural and Biological Engineering 2020年第4期13卷 1-9页

作者： Yanbo Huang USDA-ARS Crop Production Systems Research Unit StonevilleMS 38776USA

Nature-inspired algorithms have been developed with biological *** learning algorithms from artificial neurons and artificial neural networks have been developed to mimic the human brain with synthetic *** research can be traced back to the 1940s and has been expanded to agri-food problem solving in the last three ***,the research and applications have entered the stage of deep learning with more layers and neurons that have complex connections to extract deep features of the *** this paper,the developments of artificial neural networks and deep learning algorithms are presented and discussed in conjunction with their biological connections for agri-food *** related independent studies previously conducted by the author are summarized with the newly conducted being *** the same time,the algorithms motivated by recent bionics studies are compared and discussed for their potentials for agri-food production.

关键词： nature-inspired algorithm agri-food production machine learning deep learning artificial neural networks artificial intelligence

Time-series analysis with a hybrid Box-Jenkins ARIMA

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Journal of Harbin Institute of Technology(New Series) 2004年第4期11卷 413-421页

作者： Dilli R Aryal 王要武 School of Management Harbin Institute of Technology Harbin 150001 China

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

关键词： time series analysis ARIMA Box-Jenkins methodology artificial neural networks hybrid model

ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers

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能源与人工智能(英文) 2022年第1期7卷 142-148页

作者： Opeoluwa Owoyele Pinaki Pal Energy Systems Division Argonne National Laboratory9700 S Cass AvenueLemontIL 60439United States

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry *** has motivated the use of neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion ***,due to the nonlinearities and multi-scale nature of combustion,the predicted solution often diverges from the true solution when these machine learning models are coupled with a computational fluid dynamics *** is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation *** the present work,a novel neural ordinary differential equations approach to modeling chemical kinetics,termed as ChemNODE,is *** this machine learning framework,the chemical source terms predicted by the neural networks are integrated during training,and by computing the required derivatives,the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution.A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic *** is shown that ChemNODE accurately captures the chemical kinetic behavior and reproduces the results obtained using the detailed kinetic mechanism at a fraction of the computational cost.

关键词： Machine learning neural ordinary differential equations artificial neural networks Deep learning Chemical kinetics artificial neural network Chemistry solvers

artificial neural Network Performing the Forward Operation of Color Appearance Model

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Journal of Beijing Institute of Technology 2003年第S1期12卷 54-57页

作者：柴冰华廖宁放杨卫平 National Lab of Color Science and Engineering School of Information Science and TechnologyBeijing Institute of TechnologyBeijing 100081China

A method of the forward operation of color appearance (from colorimetric attributes to color appearance attributes) using an artificial neural network (ANN) is presented The neural network model developed is a multilayer feedforward neural network model for predicting color appearance model (CAM). This method greatly decreased the mathematical computation in color appearance prediction. The error backed-propagation (BP) algorithm was applied in the training of the neural networks, and it was trained and tested by the LUTCHI color appearance datasets which are the most comprehensive one in testing color appearance model. CRT was selected as a typical example in experiment because it is usually used as self-luminous object in fact, and several ways for choosing training samples were included and compared each other. The testing results show that the color appearance prediction using artificial neural network is well consistent with visual evaluation.

关键词： artificial neural networks enor-backed-propagation color appearance model

Effects of modeling means on properties of monitoring models of spot welding quality

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China Welding 2002年第2期11卷 119-123页

作者：张忠典李冬青赵洪运于燕 StateKeyLaboratoryofAdvancedWeldingProductionTechnology

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.

关键词： spot welding quality monitoring model,regression analysis artificial neural networks

SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

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Mining Science and Technology 2009年第3期19卷 385-388,394页

作者： ZHANG Hua WANG Yun-jia LI Yong-feng School of Environment ＆ Spatial lnformatics China University of Mining ＆ Technology Xuzhou Jiangsu 221116 China Jiangsu Key Laboratory of Resources and Environmental Information Engineering Xuzhou Jiangsu 221116 China

A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is *** on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence *** of the geological and mining factors involved are related in a nonlinear *** new model is based on statistical theory(SLT) and empirical risk minimization(ERM) *** data collected from observation stations were used for the learning and training *** calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) *** results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN *** LS-SVM model was faster in computation and had better generalized *** provides a highly effective method for calculating the predicting parameters of the probability-integral method.

关键词： mining subsidence probability-integral method least squares support vector machine artificial neural networks