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In this paper, a method of tank gun stability control system based on multi-dimensional Taylor network optimal control(MTN) is proposed. As the fact that tank itself has a strong nonlinear characteristic, which will lead to the traditional effects such as PID control is not satisfactory. Multi-dimensional Taylor network controller, which only relies on the output of the system, it combines the new...
A novel method for nonlinear stochastic time-varying systems identification based on multi-dimensional Taylor network with optimal structure is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as the time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output...
The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult to achieve the desired tracking performance. In this paper, a new network controller (multi-dimensional Taylor network) is proposed, which only relies on the output of system. Firstly...
The control input item is added to constitute the nonlinear dynamic model, on the basis of the original multi-dimensional Taylor network in this paper. And this nonlinear dynamic model is used to optimally control MISO nonlinear system only by output feedback without the disturbance estimation of the system or needing the state observer. The back-propagation algorithm is used to train the parameters...
A method of the empirical mode decomposition multidimensional Taylor network for establishing the dynamics model and its parameters identification is proposed for time series forecasting. By the empirical mode decomposition algorithm, the time series are decomposed into one residue signal and several intrinsic mode function signals. The multi-dimensional Taylor network models are established for sub-time...
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