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Feature Selection Based on Adaptive Fuzzy Membership Functions

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自动化学报 2006年第4期32卷 496-503页

作者： XIE Yan-Tao SANG Nong ZHANG Tian-Xu Institute for Pattern Recognition and artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

关键词： Membership function feature selection architecture pruning artificial neural networks

artificial neural network approach to assess selective flocculation on hematite and kaolinite

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International Journal of Minerals,Metallurgy and Materials 2014年第7期21卷 637-646页

作者： Lopamudra Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre R&D Tata Steel Limited Institute of Minerals and Materials Technology(Council of Scientific and Industrial Research) Department of Fuel & Minerals Engineering Indian School of Mines University

Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.

关键词： hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency

Physics-informed neural networks for estimating stress transfer mechanics in single lap joints

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2021年第8期22卷 621-631页

作者： Shivam SHARMA Rajneesh AWASTHI Yedlabala Sudhir SASTRY Pattabhi Ramaiah BUDARAPU School of Mechanical Sciences Indian Institute of TechnologyBhubaneswar 752050India Department of Aeronautical Engineering Institute of Aeronautical EngineeringHyderabad 500043India

With the explosive growth of computational resources and data generation,deep machine learning has been successfully employed in various applications.One important and emerging scientific application of deep learning involves solving differential equations.Here,physics-informed neural networks(PINNs)are developed to solve the differential equations associated with a specific scientific problem.As such,algorithms for solving the differential equations by embedding their initial and boundary conditions in the cost function of the artificial neural networks using algorithmic differentiation must also be developed.In this study,various PINNs are adopted to estimate the stresses in the tablets and the interphase of a single lap joint.The proposed model is represented by two fourth-order non-homogeneous coupled partial differential equations,with the axial stresses in the upper and lower tablets adopted as the dependent variables.The axial stresses are a function of the tablet length,which presents the independent variable.Therefore,the axial stresses in the tablets are estimated by solving the coupled partial differential equations when subjected to the boundary conditions,whereas the remaining stress components are expressed in terms of axial stresses.The results obtained using the developed methodology are validated using the results obtained via MAPLE software.

关键词： Physics-informed neural networks(PINNs) Algorithmic differentiation artificial neural networks Loss function Single lap joint

Prediction of operational parameters effect on coal flotation using artificial neural network

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Journal of University of Science and Technology Beijing 2008年第5期15卷 528-533页

作者： E. Jorjani Sh. Mesroghli S. Chehreh Chelgani Department of Mining Engineering Science and Research BranchIslamic Azad University

artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of collector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

关键词： coal flotation operational parameters artificial neural networks combustible recovery

Prediction of Infinite Dilution Activity Coefficients of Halogenated Hydrocarbons in Water

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Chinese Journal of Structural Chemistry 2008年第4期27卷 491-497页

作者：许惠英闵剑青 College of Biology & Environment Engineering Zhejiang Shuren University

Geometrical optimization and electrostatic potential calculations have been performed for a series of halogenated hydrocarbons at the HF/Gen-6d level. A number of electrostatic potentials and the statistically based structural descriptors derived from these electrostatic potentials have been obtained. Multiple linear regression analysis and artificial neural network are employed simultaneously in this paper. The result shows that the parameters derived from electrostatic 2 potentials σtot^2, V s and ∑ Vs^＋, together with the molecular volume （Vine） can be used to express the quantitative structure-infinite dilution activity coefficients （γ^∞） relationship of halogenated hydrocarbons in water. The result also demonstrates that the model obtained by using BFGS quasiNewton neural network method has much better predictive capability than that from multiple linear regression. The goodness of the model has been validated through exploring the predictive power for the external test set. The model obtained via neural network may be applied to predict γ^∞ of other halogenated hydrocarbons not present in the data set.

关键词： halogenated hydrocarbons molecular electrostatic potentials infinite dilution activity coefficients （γ^∞） QSPR artificial neural networks

A New Evaluation Criterion for Optimizing the Mechanical Properties of Toughened Polypropylene/Silica Nanocomposites

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Chinese Journal of Polymer Science 2020年第8期38卷 877-887,I0008页

作者： Hossein Pourrahmani Mona Golparvar Mohammad Fasihi Group of Energy Materials (E)cole Polytechnique Fédérale de LausanneSion 1951Switzerland School of Chemical Petroleum and Gas EngineeringIran University of Science and TechnologyTehran 16846-13114Iran School of Mechanical Engineering Braunschweig university of technologyBraunschweig 38106Germany

This study aims to experiment with the mechanical properties of polypropylene(PP)/thermoplastic elastomer/nano-silica/compatibilizer nanocomposite using the melt mixing method.The addition of polyolefin elastomers has proved to be an approachable solution for low impact strength of PP,while it would also reduce the Young's modulus and tensile strength.That is why reinforcement would be applied to this combination to enhance the elastic modulus.The mechanical properties of the prepared composites were devised to train an artificial neural network to predict these properties of the system in 6256 unknown points.Therefore,the sensitivity analysis was performed and the share of each input parameter on the respective output values was calculated.Additionally,a novel parameter called nanocomposite evaluation criterion(NEC)is introduced to analyze the suitability of the nanocomposites considering the mechanical properties.Accordingly,the formulation with optimal mechanical properties of toughness,elongation at break,tensile strength,Young's modulus,and impact strength was obtained.

关键词： Nanocomposite Polypropylene(PP) Silica artificial neural networks Nanocomposite evaluation criterion(NEC) Sensitivity analysis

Hybrid classifier for fault location in active distribution networks

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Protection and Control of Modern Power Systems 2020年第1期5卷 194-202页

作者： Sadegh Jamali Alireza Bahmanyar Siavash Ranjbar Centre of Excellence for Power System Automation and Operation School of Electrical EngineeringIran University of Science and TechnologyP.O.Box:16846-13114TehranIran

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation(DG)and microgrids.The method uses the voltage and current data from the measurement points at the main substation,and the connection points of DG and microgrids.The data is used in a single feedforward artificial neural network(ANN)to estimate the distances to fault from all the measuring points.A k-nearest neighbors(KNN)classifier then interprets the ANN outputs and estimates a single fault location.Simulation results validate the accuracy of the fault location method under different fault conditions including fault types,fault points,and fault resistances.The performance is also validated for non-synchronized measurements and measurement errors.

关键词： artificial neural networks Distributed generation Distribution networks Fault location K-nearest neighbors

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

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Frontiers of Structural and Civil Engineering 2018年第4期12卷 490-503页

作者： T.Chandra Sekhara REDDY Civil Engineering G.P.R.Engineering CollegeKurnool 518002Andhra PradeshIndia

This paper is aimed at adapting artificial neural networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

关键词： artificial neural networks root mean square error SIFCON silica fume metakaolin steel fiber

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

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World Journal of Gastroenterology 2021年第37期27卷 6191-6223页

作者： Chrysanthos D Christou Georgios Tsoulfas Organ Transplant Unit Hippokration General HospitalAristotle University of ThessalonikiThessaloniki 54622Greece

artificial intelligence(AI)is an umbrella term used to describe a cluster of interrelated fields.Machine learning(ML)refers to a model that learns from past data to predict future data.Medicine and particularly gastroenterology and hepatology,are data-rich fields with extensive data repositories,and therefore fruitful ground for AI/ML-based software applications.In this study,we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application.Specifically,we refer to the applications of AI/ML-based models in prevention,diagnosis,management,and prognosis of gastrointestinal bleeding,inflammatory bowel diseases,gastrointestinal premalignant and malignant lesions,other nonmalignant gastrointestinal lesions and diseases,hepatitis B and C infection,chronic liver diseases,hepatocellular carcinoma,cholangiocarcinoma,and primary sclerosing cholangitis.At the same time,we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them.Notably,we elaborate on the concerns regarding intrinsic biases,data protection,cybersecurity,intellectual property,liability,ethical challenges,and transparency.Even at a slower pace than anticipated,AI is infiltrating the healthcare industry.AI in healthcare will become a reality,and every physician will have to engage with it by necessity.

关键词： artificial intelligence Machine learning Gastroenterology Hepatology artificial neural networks Support vector machine