Prediction of Concrete Compressive Strength Based on the BP Neural Network Optimized by Random Forest and ISSA by Gang Chen, Donglin Zhu, Xiao Wang, Changjun Zhou, Xiangyu Chen

“…In modern engineering construction, the compressive strength of concrete determines the safety of engineering structure. BP neural network (BPNN) tends to converge to different local minimum points, and the prediction accuracy is not high in the prediction of the compressive strength of concrete. …”
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Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall by M. R. Mustafa, R. B. Rezaur, H. Rahardjo, M. H. Isa, A. Arif

“…A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. …”
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Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term by Guiying Chen, Linshan Wang

“…The stability of a class of static interval neural networks with time delay in the leakage term is investigated. …”
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An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter by Yu Dou

“…First, the validities of the BP neural network and Kalman filter are verified, respectively. …”
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Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network by Shuo Zhu, Yan Liu

“…Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. …”
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Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass by Mohammad Hasan Shojaeefard, Mostafa Akbari, Mojtaba Tahani, Foad Farhani

“…Scanning electron microscopy (SEM) and X-ray diffraction analysis were used to probe chemical compositions. An artificial neural network model was developed to simulate the correlation between the Friction Stir Lap Welding (FSLW) parameters and mechanical properties. …”
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Existence and Stability of Almost Periodic Solution for a Stochastic Cellular Neural Network with External Perturbation by Hui Zhou, Zongfu Zhou, Wei Jiang

“…A class of stochastic cellular neural networks with external perturbation is investigated. …”
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Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model by Ngoc Hung- Chu, Hoai Nam- Nguyen, Van Du- Nguyen, Dang Binh- Nguyen

“…In this paper, we have developed a two-layer artificial neural network (ANN) model for training using the Levenberg-Marquardt algorithm to predict torque during deep drilling. …”
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Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer by Youtao Gao, Tanran Zhao, Bingyu Jin, Junkang Chen, Bo Xu

“…In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. …”
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Stability Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Time-Varying Delays by Chuangxia Huang, Xinsong Yang, Yigang He

“…This paper is concerned with pth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions on pth moment exponential stability for the considered system is presented. …”
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Application of a Recurrent Neural Network and Simplified Semianalytical Method for Continuous Strain Histories Estimation by Huan Luo, Miaohua Huang, Wei Xiong

“…To obtain accurate fatigue loading in the form of continuous strain histories, a novel approach is proposed based on the combination of a recurrent neural network and simplified semianalytical method. The recurrent neural network named nonlinear autoregressive model with exogenous inputs (NLARX) is applied to determine the relationship between external loads and corresponding fatigue loading. …”
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Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network by E. Khalilzadeh Vahidi, M. M. Roshani

“…Multilayer perceptron (MLP) neural network was used to predict the maximum load in SPSW with opening. …”
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Vehicle state estimation based on extended Kalman filter and radial basis function neural networks by Yunfei Zha, Xinye Liu, Fangwu Ma, CC Liu

“…Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. …”
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Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network by Quantong ZHANG, Yao ZHENG, Liu YANG, Shuaishuai ZHANG, Quanyou GUO

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Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models by Dongmin Seo, Daekyeom Lee, Sekil Park, Sangwoo Oh

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Artificial neural network forecast application for fine particulate matter concentration using meteorological data by M. Memarianfard, A.M. Hatami, M. Memarianfard

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Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network by Jingfeng Yang, Nanfeng Zhang, Ming Li, Yanwei Zheng, Li Wang, Yong Li, Ji Yang, Yifei Xiang, Lufeng Luo

“…To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. …”
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Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete by Aref M. al-Swaidani, Waed T. Khwies

“…The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. …”
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Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network by Zhonghua Xu, Changguo Dai, Jing Wang, Lejun Liu, Lei Jiang

“…It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. …”
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Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model by Kacper Cybiński, Marcin Płodzień, Michał Tomza, Maciej Lewenstein, Alexandre Dauphin, Anna Dawid

“…Here, we show how the informed use of an interpretability method called class activation mapping, and the analysis of the latent representation of the data with the principal component analysis can increase trust in predictions of a neural network (NN) trained to classify quantum phases. …”
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