scholarly journals Hammerstein system with a stochastic input of arbitrary/unknown autocorrelation: Identification of the dynamic linear subsystem

2021 ◽  
Author(s):  
Tsair‐Chuan Lin ◽  
Kainam Thomas Wong
Keyword(s):  
Hammerstein System ◽  
Linear Subsystem ◽  
Stochastic Input

Hierarchical fractional-order Hammerstein system identification

2021 ◽  
pp. 1-13
Author(s):  
Soumaya Marzougui ◽  
Asma Atitallah ◽  
Saida Bedoui ◽  
Kamel Abderrahim
Keyword(s):  
System Identification ◽  
Fractional Order ◽  
Hammerstein System

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

2019 ◽  
Vol 41 (13) ◽  
pp. 3612-3625 ◽  
Author(s):  
Wang Qian ◽  
Wang Qiangde ◽  
Wei Chunling ◽  
Zhang Zhengqiang
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Small Neighborhood ◽  
High Order ◽  
Interconnected Systems ◽  
Delay Systems ◽  
Rbf Neural Networks ◽  
Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates

2000 ◽  
Vol 12 (2) ◽  
pp. 367-384 ◽  
Author(s):  
Hans E. Plesser ◽  
Wulfram Gerstner
Keyword(s):  
Membrane Potential ◽  
Diffusion Approximation ◽  
Hazard Function ◽  
Time Dependent ◽  
Integrate And Fire ◽  
Probability Current ◽  
Periodic Input ◽  
Stochastic Input ◽  
Free Membrane ◽  
Noise Models

We analyze the effect of noise in integrate-and-fire neurons driven by time-dependent input and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard function that has a gaussian dependence on the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.

The interchangeability of ·/M/1 queues in series

1979 ◽  
Vol 16 (3) ◽  
pp. 690-695 ◽  
Author(s):  
Richard R. Weber
Keyword(s):  
Output Process ◽  
Input Process ◽  
Waiting Room ◽  
Series Order ◽  
In Series ◽  
Queues In Series ◽  
Stochastic Input ◽  
Pass Through ◽  
Single Exponential

A series of queues consists of a number of · /M/1 queues arranged in a series order. Each queue has an infinite waiting room and a single exponential server. The rates of the servers may differ. Initially the system is empty. Customers enter the first queue according to an arbitrary stochastic input process and then pass through the queues in order: a customer leaving the first queue immediately enters the second queue, and so on. We are concerned with the stochastic output process of customer departures from the final queue. We show that the queues are interchangeable, in the sense that the output process has the same distribution for all series arrangements of the queues. The ‘output theorem' for the M/M/1 queue is a corollary of this result.

Control of arrivals to a stochastic input–output system

1980 ◽  
Vol 12 (4) ◽  
pp. 972-999 ◽  
Author(s):  
Søren Glud Johansen ◽  
Shaler Stidham
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Input Output ◽  
Distinctive Features ◽  
Control Models ◽  
Special Cases ◽  
Non Linear ◽  
Markov Decision ◽  
Output System ◽  
Stochastic Input

The problem of controlling input to a stochastic input-output system by accepting or rejecting arriving customers is analyzed as a semi-Markov decision process. Included as special cases are a GI/G/1 model and models with compound input and/or output processes, as well as several previously studied queueing-control models. We establish monotonicity of socially and individually optimal acceptance policies and the more restrictive nature of the former, with random rewards for acceptance and both customer-oriented and system-oriented non-linear waiting costs. Distinctive features of our analysis are (i) that it allows dependent interarrival times and (ii) that the monotonicity proofs do not rely on the standard concavity-preservation arguments.

Convergence of the Iterative Hammerstein System Identification Algorithm

2004 ◽  
Vol 49 (11) ◽  
pp. 1929-1940 ◽  
Author(s):  
E.-W. Bai ◽  
D. Li
Keyword(s):  
System Identification ◽  
Identification Algorithm ◽  
Hammerstein System

Robust stabilization of rigid body attitude motion in the presence of a stochastic input torque

2015 ◽  
Author(s):  
Ehsan Samiei ◽  
Maziar Izadi ◽  
Sasi P. Viswanathan ◽  
Amit K. Sanyal ◽  
Eric A. Butcher
Keyword(s):  
Rigid Body ◽  
Robust Stabilization ◽  
Attitude Motion ◽  
Body Attitude ◽  
Stochastic Input ◽  
Input Torque
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