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Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization

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Underground Space 2022年第5期7卷 833-846页

作者： Diyuan Li Zida Liu Peng Xiao Jian Zhou Danial Jahed Armaghani School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,Bayesian optimization and the synthetic minority oversampling technique+Tomek Link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.Bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%).

关键词： Rockburst prediction feedforward neural network Bayesian optimization SMOTETomek

Multiscale computation on feedforward neural network and recurrent neural network

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Frontiers of Structural and Civil Engineering 2020年第6期14卷 1285-1298页

作者： Bin LI Xiaoying ZHUANG Department of Geotechnical Engineering Tongji UniversityShanghai 200092China

Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro-or fine-scale.Among existing methods for homogenization,computational homogenization is widely used in multiscale analyses of structures and materials.Conventional computational homogenization suffers from long computing times,which substantially limits its application in analyzing engineering problems.The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response.Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set.This article intends to model the multiscale constitution using feedforward neural network(FNN)and recurrent neural network(RNN),and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths.Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method constitutive model feedforward neural network recurrent neural network

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

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能源与人工智能(英文)

能源与人工智能(英文) 2020年第2期2卷 83-100页

作者： X.J.Luo Lukumon O.Oyedele Anuoluwapo O.Ajayi Olugbenga O.Akinade Juan Manuel Davila Delgado Hakeem A.Owolabi Ashraf Ahmed Big Data Enterprise and Artificial Intelligence Laboratory(Big-DEAL) University of the West of England(UWE)Frenchay CampusBristolUnited Kingdom Department of Civil and Environmental Engineering Brunel University LondonLondonUnited Kindom

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.

关键词： Prediction Deep learning feedforward neural network Genetic algorithm Electricity consumption

The “Bottleneck” Behaviours in Linear feedforward neural network Classifiers and Their Breakthrough

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Journal of Computer Science & Technology 1999年第1期14卷 34-43页

作者：黄德双 Beijing Institute of System Engineering Beijing P.R. China

The classification mechanisms of linear feedforward neural network classifiers (FNNC), whose hidden layer performs the Fisher linear transformation of the input patterns, under the supervision of outer-supervised signals are inves- tigated. The 'bottleneck' behaviours in linear FNNCs are observed and analyzed. In addition, the structure stabilities of the linear FNNCs are also discussed. It is pointed out that the key point to break through the 'bottleneck' behaviours for lin- ear FNNCs is to change linear hidden neurons into nonlinear hidden ones. Finally, the experimental results, taking the parity 3 problem as example, are given.

关键词： feedforward neural network mean square classifiers outer-supervised signal classification

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Relations Between Wavelet network and feedforward neural network

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Journal of Southwest Jiaotong University(English Edition) 2002年第2期10卷 179-184页

作者：刘志刚何正友钱清泉 Institute of Railway Electrification & Automation Southwest Jiaotong University Chengdu 610031 China

A comparison of construction forms and base functions is made between feedforward neural network and wavelet network. The relations between them are studied from the constructions of wavelet functions or dilation functions in wavelet network by different activation functions in feedforward neural network. It is concluded that some wavelet function is equal to the linear combination of several neurons in feedforward neural network.

关键词： wavelet transformation feedforward neural network wavelet network

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Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

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Traditional Medicine Research 2023年第1期8卷 12-19页

作者： Yu Liu Ying-Fang Cui Dan-Dan Shi Shu-Li Man Xia Li Wen-Yuan Gao State Key Laboratory of Food Nutrition and Safety/Key Laboratory of Industrial Microbiology Ministry of Education/Tianjin Key Laboratory of Industry Microbiology/National and Local United Engineering Lab of Metabolic Control Fermentation Technology/China International Science and Technology Cooperation Base of Food NutritionSafety and Medicinal Chemistry/College of BiotechnologyTianjin University of Science&TechnologyTianjin 300457China Tianjin Key Laboratory for Modern Drug Delivery&High-Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China.

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different regions.Methods:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different regions.In addition,the feedforward neural network model provided a pattern classification of sample regions.Results:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,respectively.The level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other origins.After wine-processing,the glucose or fructose were dehydrated to increase the levels of 5-hydroxymethylfurfural.The C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable components.Polyphenol derivatives may be degraded to increase the content of phenolic acid.Subsequently,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample origins.Conclusion:This work provided a trustworthy method to evaluate the quality and distinguish the sources of cornus.Meanwhile,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

关键词： Cornus officinalis Sieb.et Zucc. quality evaluation fingerprints processing mechanism feedforward neural network

A new target tracking filter based on deep learning

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Chinese Journal of Aeronautics 2022年第5期35卷 11-24页

作者： Yaqi CUI You HE Tiantian TANG Yu LIU Naval Aviation University Yantai 264001China

At present,current filters can basically solve the filtering problem in target tracking,but there are still many problems such as too many filtering variants,too many filtering forms,loosely coupled with the target motion model,and so on.To solve the above problems,we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters.We firstly analyze the computation graphs of typical a-βand Kalman.Through analysis,it is concluded that a-βand Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights.Then,given this and considering that a recurrent neural network has the recognition capability for target motion patterns,a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network,recurrent neural network,and attention mechanism.And the unified tracking filter proposed in this paper can generate three aspects of unity:a unified target motion model,an adaptive filter method,and an overall track filtering framework.Finally,Simulation results show that the proposed filter is effective and useful,of which the overall performance is superior to those of compared filters.

关键词： Attention mechanism feedforward neural network Interactive multiple model Recurrent neural network Tracking filter

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Optimal design of wavy microchannel heat sinks based on prediction and multi-objective optimization algorithm

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Energy and AI

Energy and AI 2023年第4期14卷 112-129页

作者： Yuguo Fu Xueling Liu Jiansheng Wang Key Laboratory of Efficient Utilization of Low and Medium Grade Energy MOESchool of Mechanical EngineeringTianjin UniversityTianjin300354PR China Geothermal Research&Training Center Tianjin UniversityTianjin300354PR China

In this paper,the multi-objective optimization of wavy microchannel heat sinks is performed by combining numerical calculation,prediction algorithm and genetic algorithm.In numerical calculation,the fluid-solid conjugate heat transfer of heat sinks with different parameters are simulated in Fluent.On this basis,the vari-able parameters and objective parameters are used to complete the training of neural network model,which aims to achieve accurate prediction of objective parameters.Finally,the multi-objective genetic algorithm is applied to find the Pareto front according to different requirements on the foundation of the prediction model.Results show that the coefficient of determination of the neural network models are all greater than 0.85,which proves that the prediction model has a high accuracy.The Pareto fronts are obtained by non-dominated sorting genetic algorithm(NSGA-II)with different objective parameters and they reveal that the channel with the optimal performance corresponds to a larger channel width or Reynolds number.In addition,it is also found the dimensionless temperature difference is correlated with Nusselt number.

关键词： Wavy microchannel heat sinks Latin hypercube sampling feedforward neural network Multi-objective optimization Pareto front

Mass estimation method for intelligent vehicles based on fusion of machine learning and vehicle dynamic model

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Autonomous Intelligent Systems 2022年第1期2卷 50-59页

作者： Zhuoping Yu Xinchen Hou Bo Leng Yuyao Huang School of Automotive Studies Tongji UniversityShanghai201804China

Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.

关键词： Vehicle mass estimation Machine learning feedforward neural network Vehicle dynamics Intelligent vehicle