With the main objective to develop an iterative state-space identification algorithm for linear multivariable discrete time-variant systems, in this study we propose and implement a computational procedure we call MOE...
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In this article proposal for solving the Discrete-Time Algebraic Riccati Equation (DARE) using a multilayer Recurrent Neural Network (RNN) approach is presented. Systems of coupled matricial nonlinear differential equ...
详细信息
With the main objective to develop an iterative state-space identification algorithm for linear multivariable discrete time-variant systems, in this study we propose and implement a computational procedure we call MOE...
详细信息
With the main objective to develop an iterative state-space identification algorithm for linear multivariable discrete time-variant systems, in this study we propose and implement a computational procedure we call MOESP-VAR that is based in subspace methods of the type "multivariable output-error state space" (MOESP). In addition the algorithm is tested with criterion and experimentation that we also propose
In this article proposal for solving the discrete-time algebraic Riccati equation (DARE) using a multilayer recurrent neural network (RNN) approach is presented. Systems of coupled matricial nonlinear differential equ...
详细信息
In this article proposal for solving the discrete-time algebraic Riccati equation (DARE) using a multilayer recurrent neural network (RNN) approach is presented. Systems of coupled matricial nonlinear differential equations are derived describing the neural dynamics of the Neuro-riccati equation. By solving these coupled matrix equations using recurrent neural networks a symmetric and positive definite solution is obtained. Several examples demonstrate the effectiveness of this proposal and respective implementation.
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