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A novel method utilizing state space neural network (SSNN) with adaptive filters is proposed to estimate the traffic flow parameters. The SSNN's network topology is derived from delays and stops estimation problem, so the design of SSNN reflects the relationships that exist in physical traffic systems. To improve SSNN effectiveness, the adaptive filters is proposed to train the SSNN instead of conventional...
The AQM (Active Queue Management) Algorithm such as RED, PI, REM have too long respond time and some other problems. Though it makes some enhancement, the PID (Proportional Integral Differential) active queue management algorithm can not make the queue length converge to expectation with longer delay. Because of self-adapt control of BP (back-propagation) neural network, this issue bring forward a...
This paper presents a grey predictive control method for a class of nonlinear systems with unknown input delay. By using BP neural network, the unknown input delay is identified firstly. The system output is then estimated by the grey predictive algorithm. The output feedback control is fulfilled by PID algorithm which is used to tune its three parameters. By means of combining grey predictive algorithm...
For multi-variable nonlinear system evolution with time-varying, a neural network model based on multi-variable phase-space reconstruction has been proposed, and is used in civil engineering for synthesized deformation prediction of deep foundation pit. By the various time series time delay and embedding dimension determined respectively in this model, the multi-variable series of excavation deformation...
With the ever-increasing number of multicast data applications recently, considerable efforts have been focused on the design of flow control schemes for multicast services. The main difficulties in designing a flow controller for multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat...
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