青云翻译

The neural network to achieve the strong multiple-input multiple-output coupled nonlinear discrete systems with linear decoupling problem does not require precise mathematical model, together constitute the inverse system by static neural networks and the characterization of the delay of the delay f

Above using neural networks to multiple-input multiple-output linearization and decoupling problem of strongly coupled nonlinear discrete system does not need to know precise mathematical model of the system, and characterization by static neural network inverse system delay delay factors constitute

Above uses the neural network to realize inputs the multi-output close coupling non-linearity separate system linearization decoupling question not to need to know precisely the system the mathematical model, its counter system constitutes together by the static state neural network and the attribut

In the neural network to achieve multi-input multi-fuel supply a strong non-linear discrete systems coupled linear, decoupled issues do not need to know that the system of precise mathematical models, and its counter-system by static neural networks with delay and the time delay factor, a relatively

Above using neural networks to multiple-input multiple-output linearization and decoupling problem of strongly coupled nonlinear discrete system does not need to know precise mathematical model of the system, and characterization by static neural network inverse system delay delay factors constitute