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Under the opportunistic network environments, normal network protocols can hardly transfer data successfully and efficiently. In order to overcome this problem, many new routing algorithms have been raised pertinently. Thereinto, the Epidemic routing algorithm is a typical approach, which maintains least delay with a highest delivery ratio. However, it is a defective method for routing data as it...
Channel shortening methods, in multicarrier systems, are applied for decreasing and almost compensating for the inter-symbol and inter-carrier interferences due to the channel delay spread. In this paper, we propose a new channel shortening technique for orthogonal frequency division multiplexing (OFDM) systems based. The propoed method is based on the neural network equipped with the Perceptron learning...
The four-stage evaporator is the core of the process in the manufacture of concentrated grape juice. The dynamic features of this process are very complex due to inputs and outputs constraints, time delays, loop interactions and the persistent unmeasured disturbances that affect it. Therefore, this kind of process requires a robust control in order to assure a stable operation taking into account...
This article applies iterative learning control (ILC) to road simulation test system and simulates the control system to reproduce a stochastic pavement profile. With uniform white noise as input, using actual measured input-output data and dynamic neural network, system nonlinear autoregressive moving average model (NARMA) was established. Regarding road simulator control mission as a perfect tracking...
In this paper the time-delay neural networks (TDNNs) have been implemented in detecting the damage in bridge structure using vibration signature analysis. A simulation study has been carried out for the incomplete measurement data. It has been observed that TDNNs have performed better than traditional neural networks in this application and the arithmetic of the TDNNs is simple.
In this paper, a quasi-sliding mode variable structure control algorithm is combined with RBF neural network. So, the strong robustness of the quasi-sliding mode variable structure algorithm and the property of adaptive learning supplying from RBF neural network algorithm are combined. The algorithm is subsequent inducted into the large time-delay system. Simulation results show that the strategy...
Against networked motion control system with the characteristics of uncertain delay, in view of control, through using generalized predictive control algorithm, combining with the buffer and event-time driven, we provide a comprehensive compensation control solution, which solves random network transmission delay, time-series confusion and data packet loss, so effectively overcome uncertainty of actual...
Since the characteristics of time-delay, nonlinear and uncertain model structure, and in order to control temperature in greenhouse better, a fuzzy neural network PID control is presented in this paper. The intelligent controller can adjust the parameters according to the variation of system characteristics even if the system model is unknown, and meet the requests of real-time control. The approach...
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