Global μ-Stability of Impulsive Complex-Valued Neural Networks with Leakage Delay and Mixed Delays by Xiaofeng Chen, Qiankun Song, Yurong Liu, Zhenjiang Zhao

“…The impulsive complex-valued neural networks with three kinds of time delays including leakage delay, discrete delay, and distributed delay are considered. …”
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Neural Network Based Adaptive Backstepping Control for Electro-Hydraulic Servo System Position Tracking by Zhenshuai Wan, Longwang Yue, Yu Fu

“…To cope with this issue, an adaptive backstepping controller based on neural network (NN) is proposed in this paper. A radial-basis-function neural network (RBF NN) is constructed to approximate the lumped uncertainties caused by modeling uncertainties and external disturbances, where the adaptive law is adopted to adjust controller parameters online. …”
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Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images by N.A. Firsov, V.V. Podlipnov, N.A. Ivliev, D.D. Ryskova, A.V. Pirogov, A.A. Muzyka, A.R. Makarov, V.E. Lobanov, V.I. Platonov, A.N. Babichev, V.A. Monastyrskiy, V.I. Olgarenko, D.P. Nikolaev, R.V. Skidanov, A.V. Nikonorov, N.L. Kazanskiy, V.A. Soyfer

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Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays by Chuangxia Huang, Lehua Huang, Yigang He

“…This paper addresses the issue of mean square exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN), whose state variables are described by stochastic nonlinear integrodifferential equations. …”
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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

Subjects: “…Testing convolutional neural networks (CNNs)…”
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Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event-Triggered Strategy by Jin-E Zhang, Huan Liu

“…This paper proposes the event-triggered strategy (ETS) for multiple neural networks (NNs) with parameter uncertainty and time delay. …”
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Enhanced capsule neural network with advanced triangulation topology aggregation optimizer for music genre classification by Linlin Jiang, Lei Yang, Shakiba azimi

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An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants by Ying-Chung Wang, Chiang-Ju Chien

“…We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. …”
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Retracted: Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network by Journal of Mathematics

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Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

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State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks by Li Zhang, Min Zheng, Dajun Du, Yihuan Li, Minrui Fei, Yuanjun Guo, Kang Li

“…Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. …”
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An Impedance Matching Method Based on Optimized BP Neural Network for Vehicular Power Line System by Renfa Song, Hanyan Li, Yongtao Li

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Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile by Naftali Goldshleger, Alexandra Chudnovsky, Eyal Ben-Dor

“…The spectral properties of the crust formed on the soil surface were analyzed using an artificial neural network (ANN). Results were compared to a study with the same population in which partial least-squares (PLS) regression was applied. …”
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

“…In this study, we propose a novel IL-6-inducing peptide prediction method called DGIL-6, which integrates 3D structural information with graph neural networks. DGIL-6 represents a peptide sequence as a graph, where each amino acid is treated as a node, and the adjacency matrix, representing the relationships between nodes, is derived from the predicted residue contact graph of the peptide sequence. …”
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Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds by Jing Huang, Xuenan Zhang, Hang Yang, Zhenbiao Li, Zhengfang Xue, Qingqing Wang, Xinyuan Zhang, Shenghua Ding, Zisheng Luo, Yanqun Xu

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Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network by Yanli Yang, Peiying Fu

“…A method based on wavelet and deep neural network for rolling-element bearing fault data automatic clustering is proposed. …”
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Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal by Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU

“…To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.…”
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An Unconventional Approach for Analyzing the Mechanical Properties of Natural Fiber Composite Using Convolutional Neural Network by Govindaraj Ramkumar, Satyajeet Sahoo, G. Anitha, S. Ramesh, P. Nirmala, M. Tamilselvi, Ram Subbiah, S. Rajkumar

“…The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. …”
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Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions by Ignacio Fernández Anitzine, Juan Antonio Romo Argota, Fernado Pérez Fontán

“…This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. …”
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Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation by Reyhaneh Dehghan, Marjan Naderan, Seyed Enayatallah Alavi

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