Estimation of nonlinear airship parameters using modular neural network
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111,197 citations
"Estimation of nonlinear airship par..." refers methods in this paper
...In this case, Adam optimization algorithm [14] is used instead of the classical stochastic gradient-descent procedure because it combines best properties of AdaGrad and RMSProp algorithms and can handle sparse gradients on noisy algorithms....
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18,794 citations
"Estimation of nonlinear airship par..." refers background in this paper
...The main advantage of using FFNN is its ability to map any nonlinear function [10]....
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...any nonlinear function can be computed and learnt by the network [10]....
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1,206 citations
"Estimation of nonlinear airship par..." refers background in this paper
...For parameter estimation of airship, the input variable to the module or FFNN is α and the response variable is unknown nonlinear airship parameter from (4)....
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...In each modular neural network, modules or independent FFNNs are integrated with a dot product layer to output aerodynamic coefficient....
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...The main advantage of using FFNN is its ability to map any nonlinear function [10]....
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...FFNNs are composed of group of neurons that are arranged into input, hidden and output layers....
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...Thus, each FFNN is composed of 1 node in input layer, 5 nodes in hidden layer and 1 node in output layer....
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147 citations
"Estimation of nonlinear airship par..." refers background in this paper
..., with τ as the sampling interval [15]....
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77 citations
"Estimation of nonlinear airship par..." refers methods in this paper
...Parameter estimation for aerial vehicles is carried out using conventional methods such as Least squares, Maximum Likelihood Estimation [1], [2] and unconventional methods such as Artificial Neural Network (ANN) [3], [4], Neural Gauss-Newton [5], [6] and Recurrent Neural Network [7]....
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