Discussion for H-Matrices and It’s Application by Guichun Han, Huishuang Gao, Haitao Yang

“…Nonsingular H-matrices and positive stable matrices play an important role in the stability of neural network system. In this paper, some criteria for nonsingular H-matrices are obtained by the theory of diagonally dominant matrices and the obtained result is introduced into identifying the stability of neural networks. …”
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A CRITIC-BPNN APPROACH TO FRICTION STIR WELDING PARAMETRIC SELECTION AND PREDICTION USING AA6082-T6 MATERIAL by Mufutau Adeniyi Abolarin, Adeyinka Oluwo, John Rajan, Swaminathan Jose, Sunday Ayoola Oke |, Alexander Iwodi Agada, Ayomide Sunday Ibitoye

“…For the back propagation neural network model, a four-layer network with sigmoid hidden neurons and output neurons was selected. …”
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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation by Yang Li, Qiuyi Zhang, Tianzhuo Gong

“…Neural networks with LSTM layers have lower errors in predicting market economic correlates than neural network methods without LSTM layers. …”
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Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia by Li Tiancheng, Ren Qing-dao-er-ji, Qiu Ying

“…Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. …”
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Application Study of Sigmoid Regularization Method in Coke Quality Prediction by Shaohong Yan, Hailong Zhao, Liangxu Liu, Qiaozhi Sang, Peng Chen, Jie Li

“…The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. …”
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Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation by Chi Hua, Erxi Zhu, Liang Kuang, Dechang Pi

“…In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. …”
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Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia by Yousef Farshidi, Reza Ghasemi, Aminin Sharafian Ardekani

“…In order to adjust the neural network weights, the error back propagation learning algorithm was implemented. …”
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Study on Adiabatic Temperature Rise Reflecting Hydration Degree of Concrete by Yanhua Han, Shaojun Fu, Shufa Wang, Zuowei Xie

“…Therefore, BP neural network method is an effective way to calculate the thermal parameters, and BP inversion algorithm provides a new way for accurately study the temperature profile of mass concrete structures.…”
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Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man by Tse Guan Tan, Jason Teo, Kim On Chin

“…Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. …”
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Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation by Rui Yuan, Zhendong Zhang, Yanyan Le, Enqing Chen

“…The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. …”
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Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms by Yuanyu Ye, Aichao Yang, Yu Wu, Chen Hu, Min Li, Yan Li, Xiaosong Deng

“…The simulation results show that the Prophet model is more suitable for the prediction of electronic transformer measurement error than the backpropagation neural network.…”
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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…In this paper, a deep neural network model based on U-Net neural network architecture is proposed for brain glioma segmentation. …”
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Forecasting Stock Prices of Companies Producing Solar Panels Using Machine Learning Methods by Zaffar A. Shaikh, Andrey Kraikin, Alexey Mikhaylov, Gabor Pinter

“…The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. …”
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Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model by Kai Wang, Kangnan Li, Feng Du

“…Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. …”
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Two-Phase Model of Multistep Forecasting of Traffic State Reliability by Jufen Yang, Zhigang Liu, Guiyan Jiang, Lin Zhu

“…Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. …”
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A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand by Azat Balapan, Rauan Yeralkhan, Alikhan Aryslanov, Gulnur Kalimuldina, Azamat Yeshmukhametov

“…To capitalize on these benefits, we propose a novel machine learning approach that represents time-series data as two-dimensional images processed using a two-dimensional convolutional neural network (2D CNN). This method is compared against the traditional one-dimensional convolutional neural network (1D CNN) method. …”
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Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture by Chaohui Wang, Songyuan Tan, Qian Chen, Jiguo Han, Liang Song, Yi Fu

“…On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. …”
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Exploring the Main Effect of e-Banking on the Banking Industry Concentration Degree on Predicting the Future of the Banking Industry: A Case Study by Zohreh Ghasemi, Mozhdeh Afshar Kermani, Tofigh Allahviranloo

“…In the developed method, the neural network parameters such as weights and errors have been considered as the fuzzy parameters to model it under uncertainty. …”
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Optimization of Aircraft Flight Trajectory Combined with Thinking Navigation Algorithm by Yifan Yang

“…Moreover, this paper chooses a simple network structure and uses the MEA to select the optimal initial parameters of the model for the BP neural network, which can avoid the local optimization of the BP neural network results. …”
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