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We consider long-haul sensor networks where sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network...
Aimed at the controlled object's character of long delay-time, this paper presents a strategy that the long delay-time object is remodeled into short delay-time object through decreasing time-delay control, after the adoption of BP neural network PID controller to control the transformed system. In this paper, the three-layer BP neural network is designed and BP neural network PID controller algorithm...
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...
The chaos-dynamic time delay BP neural network model is built to realize long-term prediction of rock mass displacement of large underground grave, and fast analyze long-time stability of surrounding rock through optimized structure of BP neural network coupling with chaotic-dynamic parameters of displacement. Embedding dimension m is set as the number of input layer, and predicting feedback mode...
This paper presents a special nonlinear switched Griffiths-Jim beamformer (SGJBF) structure. The main objective of this paper is to reduce the background noise from an acquired speech signal. The interference we considered here is non-stationary in nature and can arrive from a variety of potential sources; for example, competing talkers, radio, TV and so on. In this paper, we propose an adaptive Time...
According to the characteristics of heat supply and the demands of energy-saving control, predictive control based on BP model and gold section method is proposed. BP model is trained with real data from heat supply system, and then solving control rate by gold section algorithm. This control strategy uses rolling optimization and feedback correction, it effectively overcomes the problems of time-delay,...
Various semi-active control devices have been widely investigated to reduce the responses of structures under earthquake, including variable orifice dampers, and controllable fluid dampers (e. g. MR dampers). Due to the inherent nonlinear nature and uncertainty of the semi-active control system based on magnetorheological (MR) fluid damper, an improved back propagation (BP) algorithm is proposed,...
In order to effectively restrain the impact of network delays which are brought because of bandwidth constraint, path loss, signal attenuation and ambient noise in wireless networked control systems (WNCS), aiming at time-variant network delays and imprecise Smith predictor models, a new approach is proposed that novel Smith dynamic predictor combined with back propagation neural network (BPNN) control...
A new maximum power point tracking (MPPT) controller using artificial neural networks (ANN) for variable speed wind energy conversion system (WECS) is proposed. The algorithm uses Jordan recurrent ANN and is trained online using back propagation. The inputs to the networks are the instantaneous output power, maximum output power, rotor speed and wind speed, and the output is the rotor speed command...
Proper control of the wood-drying kiln is crucial to ensure the satisfactory quality of dried wood and in minimizing drying time and energy. This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating variable structure control (VSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description...
This paper aims at time-variant or uncertain network delay in the networked control systems (NCS), a novel approach is proposed that new Smith predictor combined with back propagation neural network (BPNN) control.Because this new Smith predictor doesnpsilat include network delay model, therefore network delay doesnpsilat need to be measured, identified or estimated on-line, it is applicable to some...
To realize the adaptive control in the heat tunnel system of fire detector, an adaptive PID control algorithm based on BP network and immune genetic algorithm (IGA) is presented. Firstly, the immune genetic algorithm is used to optimize the weights of BP network, which reduces the influence of control effect due to initial network weights; secondly, the PID parameters are adjusted on line based on...
The physical mechanism of three-tank water level system is analyzed, and an approximated linear model is deduced to represent the dynamic property of the water level system around an equilibrium point. On the other hand, Three different identification methods based on different model structures: linear model, BP neural network model, nonlinear Hammerstein ARMAX model are used to identify the dynamical...
This Paper presents a method for predicting power system voltage harmonics using Time-Delay Neural Network (TDNN). The TDNN method is based on the back propagation learning technique for feedforward neural networks. This approach has certain advantages over other conventional schemes, including the potential to track the harmonics in the time-varying power system environment. In order to demonstrate...
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