Titlebook: Discrete-Time High Order Neural Control; Trained with Kalman Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Louki Book 2008 Springer-Verlag

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Discrete-Time Block Control,on of the dynamic system is named as the model. Basically there are two ways to obtain a model; it can be derived in a deductive manner using physics laws, or it can be inferred from a set of data collected during a practical experiment. The first method can be simple, but in many cases it is excess

延期

BROOK

阻撓

Discrete-Time Block Control, chapter, a recurrent high order neural network is first used to identify the plant model, then based on this neural model, a discrete-time control law, which combines discrete-time block control and sliding modes techniques, is derived. The chapter also includes the respective stability analysis fo

Conflict

Discrete-Time Neural Observers,e observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the

偽善

Discrete-Time Output Trajectory Tracking,RHONO. This observer is based on a discrete-time recurrent high-order neural network (RHONN), which estimates the state of the unknown plant dynamics. The learning algorithm for the RHONN is based on an EKF. Once the neural network structure is determined, the backstepping and the block control tech

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沉思的魚

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opportune

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透明

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