“…Unlike RBFNN, the structure of GRNN has 4 layers: input layer, pattern layer, summation layer and output layer [47] , [48] . The regression of GRNN on the inputs is different from the least-square superposition of Gaussian weights in RBF, which uses the density function to predict the output by using the calculation of the pattern layer and the summation layer [49] , [50] , [51] . The network structure of GRNN is shown in Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, the neuronal structure of GRNN is 4-144-2-1. The setting of the neuron structure of GRNN is referred to the literatures [47] , [48] , [49] , [50] , [51] and is not described here due to the limitation of space. Figure 5 The network structure of GRNN.…”
“…Unlike RBFNN, the structure of GRNN has 4 layers: input layer, pattern layer, summation layer and output layer [47] , [48] . The regression of GRNN on the inputs is different from the least-square superposition of Gaussian weights in RBF, which uses the density function to predict the output by using the calculation of the pattern layer and the summation layer [49] , [50] , [51] . The network structure of GRNN is shown in Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, the neuronal structure of GRNN is 4-144-2-1. The setting of the neuron structure of GRNN is referred to the literatures [47] , [48] , [49] , [50] , [51] and is not described here due to the limitation of space. Figure 5 The network structure of GRNN.…”
“…The network is fully defined if its architecture, i.e., the method of connection of basic elements is specified, and the algorithm of its training is adopted in accordance with the method of training [17]. Neural networks allow creating a model of the object that accurately conveys its dynamics, while not requiring additional knowledge about the structure and parameters of the object [18]. Only the input and output signal values are the necessary data, so the object is represented as a black box.…”
The article deals with the artificial multilayer neural network with direct signal transmission and reverse error propagation, which operates in the mode of identification of static and dynamic characteristics of the electric drive. To study processes of nearly identical, nanoprene of neuroprogenitor has been developed by the research stand, which is described in the article. As a result of research on the stand, the results show that the neural network is well enough to identify the static and dynamic characteristics of the electric drive.
“…The dense layer used 'Sigmoid' as an activation function, and the 'Adam' optimization algorithm was used to train the algorithm [62]. For details regarding CNN, readers refer to the works of Matsugu et al [63], Simard et al [64], Cecotti [65], and Rohit and Chakravarthy [66].…”
Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.
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