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The nonlinear part is known inaccurately, it is irreducible to an input of the linear block, and generally does not satisfy sector restrictions.
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More precisely, the neuron activation functions are assumed to be more general and satisfy sector-like nonlinearities.
The MJHNN model, whose neuron activation function and nonlinear perturbation of the measurement equation satisfy sector-bounded conditions, is first considered and it is more general than those models studied in the literature.
A general nonlinearity model is proposed, the generalization lies in three aspects: (1) the nonlinearities include both state and delayed state information; (2) the nonlinearities satisfy sector-bounded conditions; (3) apart from the lower and upper bounds, the probability information of the nonlinearities belonging to different sector bounds is utilized.
It is assumed that the nonlinearities satisfy a sector condition as well as an incremental quadratic constraint.
In this paper, a full-order dynamic output feedback controller is designed for the delayed uncertain nonlinear system approximated by the neural network (e.g. multilayer perceptron, recurrent neural network, etc)., of which the activation functions satisfy the sector conditions.
This study offers D&B contractors feedback relating to clients' expectations of their service delivery and the areas of service that need to be improved in order to provide quality service that will satisfy public sector clients.
The parameter uncertainties enter into all the system matrices, the time-varying delay is unknown with given low and upper bounds, the nonlinearities satisfy the sector conditions, and the missing measurements are described by a binary switching sequence that obeys a conditional probability distribution.
The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions.
The genetic regulatory functions are supposed to satisfy the sector-like condition.
The activation function is assumed to satisfy a sector-bounded condition.
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