Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment by Lijun Song, Zhongxing Duan, Bo He, Zhe Li

“…In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. …”
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Adaptive Chebyshev Neural Network Control for Ventilator Model under the Complex Mine Environment by Ranhui Liu, Xinyan Hu, Chengyuan Zhang, Chuanxi Liu

“…Then, an adaptive Chebyshev neural network (ACNN) controller is proposed to effectively control the ventilator system where the unknown load torque and the unknown disturbance caused by the complex environment under the shaft are approximated by the Chebyshev neural network (CNN). …”
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A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems by Agustín Flores, Eduardo Quiles, Emilio García, Francisco Morant, Antonio Correcher

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Finding Neural Biomarkers for Motor Learning and Rehabilitation Using an Explainable Graph Neural Network by J. Han, A. Embs, F. Nardi, S. Haar, A. A. Faisal

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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

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A Deep Neural Network-Based Fault Detection Scheme for Aircraft IMU Sensors by Yiming Zhang, Hang Zhao, Jinyi Ma, Yunmei Zhao, Yiqun Dong, Jianliang Ai

“…This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). …”
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Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication by Enrique Argones Rúa, Tim Vanhamme, Davy Preuveneers, Wouter Joosen

“…One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well‐known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. …”
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Synchronization in Array of Coupled Neural Networks with Unbounded Distributed Delay and Limited Transmission Efficiency by Xinsong Yang, Mengzhe Zhou, Jinde Cao

“…This paper investigates global synchronization in an array of coupled neural networks with time-varying delays and unbounded distributed delays. …”
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Evaluating Item Content and Scale Characteristics Using a Pretrained Neural Network Model by Jeffrey Stanton, Angela Ramnarine-Rieks, Yisi Sang

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ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network by Jingming Xia, Dawei Xuan, Ling Tan, Luping Xing

“…With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. …”
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Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction by Andreas M. Billert, Runyao Yu, Stefan Erschen, Michael Frey, Frank Gauterin

Subjects: “…convolutional and recurrent neural network…”
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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

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Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model by Guillermo Napoleón Pelaez-Diaz, Rosa Vílchez-Vásquez, Antonio Huaman-Osorio, R. Mahaveerakannan, S. Pushpa, Nilesh Shelke, Sumitha Jagadibabu, Jenifer Mahilraj

“…We used a deep learning-based convolution neural network (CNN) classifier model to optimize the CNN algorithm parameter for better prediction. …”
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A graph neural network approach for hierarchical mapping of breast cancer protein communities by Xiao Zhang, Qian Liu

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Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model by Yuanyang Teng, Yicun Li, Xiaobo Wu

“…This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm.” …”
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Ranking of Search Requests in the Digital Information Retrieval System Based on Dynamic Neural Networks by Viera Bartosova, Svetlana Drobyazko, Sergii Bogachov, Olga Afanasieva, Maria Mikhailova

“…The algorithm of functioning of the neural network ranking unit based on Hopfield neural network is built. …”
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Multi-objective design of multi-material truss lattices utilizing graph neural networks by Ramón Frey, Michael R. Tucker, Mohamadreza Afrasiabi, Markus Bambach

“…In this work, we propose a novel approach that incorporates material properties as edge features within the graph representation of multi-material truss lattices, utilizing graph neural networks (GNNs) to develop a fast and efficient inverse design framework. …”
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Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model by Gun Lee, Dongkyun Kim, Hyun-Han Kwon, Eunsoo Choi

“…For estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. …”
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Rolling Force Prediction in Heavy Plate Rolling Based on Uniform Differential Neural Network by Fei Zhang, Yuntao Zhao, Jian Shao

“…When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.…”
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Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks by Hyeongwon Cho, Soongyu Kang, Yunseong Sim, Seongjoo Lee, Yunho Jung

“…In this study, we propose a lightweight fall detection method using a continuous-wave (CW) radar sensor and a binarized neural network (BNN) to meet these requirements. We used a CW radar sensor, which is more affordable than other types of radar sensors, and employed a BNN with binarized features and parameters to reduce memory usage and make the system lighter. …”
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