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J. Cent. South Univ. (2020) 27: 1754-1769 DOI: https://doi.org/10.1007/s11771-020-4405-z Three-dimensional neural network tracking control of autonomous underwater vehicles with input saturation XU... saturation model is explored to tackle the issue of input saturation. Combined with backstepping design techniques, the neural network control method and an adaptive control approach are used to estimate......
Hall, 1999: 100-200. [22] KIM C Y, BAE G J, HONG S W, PARK C H, MOON H K, SHIN H. Neural network based prediction of ground surface settlements due to tunneling [J]. Computers and Geotechnics, 2001...J. Cent. South Univ. Technol. (2011) 18: 1976-1984 DOI: 10.1007/s11771-011-0931-z Artificial neural network modeling of gold dissolution in cyanide media S. Khoshjavan1, M. Mazloumi2, B. Rezai1 1......
J. Cent. South Univ. (2016) 23: 808-816 DOI: 10.1007/s11771-016-3127-8 Actuator fault diagnosis of autonomous underwater vehicle based on improved elman neural network SUN Yu-shan(孙玉山)1, 2, LI Yue... corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which......
integrates FEM equivalent model based on previous study, the artificial neural network response surface, and the genetic algorithm. First, a multi-step press bend forming FEM equivalent model was established, with which the FEM experiments designed with Taguchi method were performed. Then, the BP neural network response surface was developed with the sample data from the FEM experiments. Furthermore......
Decentralized adaptive neural network sliding mode position/force controller design Define the joint tracking error ei, the converted desired torque the converted torque error eif and the sliding mode surface...J. Cent. South Univ. (2016) 23: 2917-2925 DOI: 10.1007/s11771-016-3355-y Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators LI Yuan......
J. Cent. South Univ. Technol. (2008) 15: 136-140 DOI: 10.1007/s11771-008-0027-6 Application of neural network to prediction of plate finish cooling temperature WANG Bing-xing...: To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system......
and the practical measured values under acceleration/deceleration conditions. RBF neural network can be extremely similar with any nonlinear function that can overcome the air flow sensor response... the control of the precision of air-fuel ratio (AFR) of port fuel injection (PFI) spark ignition (SI) engines, a chaos radial basis function (RBF) neural network is used to predict the air intake flow......
J. Cent. South Univ. (2016) 23: 869-879 DOI: 10.1007/s11771-016-3134-9 Dynamic rupture and crushing of an extruded tube using artificial neural network (ANN) approximation method Javad Marzbanrad1... method. The tube material is aluminum EN AW-7108 T6 and its length and diameter are 300 mm and 50 mm, respectively. Using the artificial neural network (ANN), the most important surfaces of energy......
of a quadrotor. The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output. Neural network estimates the sliding... to error and error derivative, estimates the altitude sliding surface variable adaptively. Then, the output of neural network is used to produce the altitude control signal of quadrotor. Simulation......
with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based... is applied to GM(1, N). In light of these conclusions, the use of time response model based on neural network is more reasonable. Eqn.(3) is transformed as follows. It is mapped to a BP neural network......