The proposed technique reduces the memory amount required to construct the predictor taking into account the intrinsic redundancy of the input-output pairs of ...

The proposed technique reduces the memory amount required to construct the predictor taking into account the intrinsic redundancy of the input-output pairs of ...

Jun 1, 1987 · The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing.

In Nonlinear Model Predictive Control, trained data are available from nonlinear process identification and used to control the nonlinear system for different ...

The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting ...

This paper aims to develop a NODE model and construct an MPC based on this novel continuous-time neural network model.

This paper investigates the identification of discrete-time nonlinear systems using neural networks with a single hidden layer.

The proposed scheme can process data information generated by the most complicated nonlinear dynamical systems such as chaotic Lorenz63 system even with noise, ...

This chapter discusses a new method for applying neural networks to control of nonlinear systems. Contrast to a conventional method, the new method does not ...

modeling method for identifying nonlinear systems using output-only ... Networks along with Deep Neural Networks are also effective in nonlinear system.