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A new optimized GA-RBF neural network algorithm. [PDF]
Comput Intell Neurosci, 2014 When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and ...
Jia W +5 more
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Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
Applied Sciences, 2023 A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction.
Xiaowei Tang, Bing Xu, Zichen Xu
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Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China [PDF]
E3S Web of Conferences, 2021 Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult..
Zhang Hui, Zhao Jixuan, Chen Chong
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Spatio-Temporal RBF Neural Networks [PDF]
2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), 2018 Published in 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)
Khan, Shujaat +4 more
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Sensors, 2021 Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the ...
Qin Yang +5 more
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Radial Basis Function Neural Network Model Based on Lasso Sparse Learning [PDF]
Jisuanji gongcheng, 2019 The traditional Radial Basis Function(RBF) neural network model uses all hidden layer nodes to construct the model.In this case,the generalization performance of traditional RBF neural network model is degraded because of the lackness of the effective ...
CUI Chen,DENG Zhaohong,WANG Shitong
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Design of RBF Adaptive Sliding Mode Controller for A Supercavitating Vehicle
IEEE Access, 2021 This paper proposes an adaptive sliding mode control strategy based on RBF (Radial Basis Function) neural network for the supercavitating vehicle system with model uncertainties and external disturbance.
Wang Jinghua +4 more
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Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management
Journal of Control Science and Engineering, 2022 Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is
Yunbing Liu
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Integrated neural network model with pre-RBF kernels [PDF]
Science Progress, 2021 To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network.
Hui Wen +3 more
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A Two-Phase Evolutionary Method to Train RBF Networks
Applied Sciences, 2022 This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic ...
Ioannis G. Tsoulos +2 more
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