Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix

MSAFIS: an evolving fuzzy inference system

“…In [1], the author uses an induced L∞ approach to create a new filter with a finite impulse response structure for state-space models with external disturbances. The model predictive stabilization problem for Takagi-Sugeno fuzzy multilayer neural networks with general terminal weighting matrix are investigated in [2]. In [3], an error passivation approach is used to derive a new passive and exponential filter for switched Hopfield neural networks with time-delay and noise disturbance.…”

Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks With Time-Varying Delay

“…Remark 13: Some results on the receding horizon stabilization and disturbance attenuation for neural works have been reported in [49]- [51]. However, these results were restricted to neural networks without time delays.…”

“…Using the arguments in [49], we can easily show that the neural network (1) with the receding horizon stabilizer is asymptotically stable under condition (18). From the Schur complement, condition (17) …”

“…The receding horizon control idea was introduced in [45]- [48] to solve new filtering and output feedback control problems. Recently, a receding horizon stabilizer was proposed for neural networks in [49]. Based on the linear differential inclusion of neural networks, the receding horizon stabilization problem for unknown nonlinear systems was solved in [50].…”

“…Based on integral Wirtingertype inequalities and the lower bounds lemma proposed in [52], a new condition on the terminal weighting matrices of a new finite horizon cost functional for the receding horizon stabilization is established, ensuring the asymptotic stability of the given neural networks. The cost functional taken in this paper is different from the existing forms used in [49]- [51], which considered the receding horizon stabilization and disturbance attenuation problems for neural networks. The cost functional in this paper has additional terminal weighting matrices on the delayed state and its time-derivative.…”