The proton exchange membrane (PEM) fuel cells have attracted significant attention due to high efficiency and low pollution. The performance and lifetime of the fuel cell power system depend on the heat and water mana...
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The agents connected by networks are capable to reach a prescribed state if only a small fraction of them are controlled with feedback information. The present work tries to stabilize groups of non-linear agents in a ...
A blind least-mean-squares (BLMS) algorithm is proposed for the parameter identification of single-input multiple-output (SIMO) systems. Without requiring knowledge of a reference signal, it is proved that the present...
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An efficient evaluation index system and evaluation model is crucial for effectiveness evaluation of the air defense networked fire controlsystem. This paper, based on pre-research for functions and characteristics o...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model,...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model, which just take the values of targets, UAVs and weapons into account, the fuel consumption is added into consideration to make the model much more practical. An improved genetic algorithm is proposed for such a multi-UAVs multi-targets task allocation. Simulation results show that the algorithm is significantly effective and the allocation result is reasonable.
Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.
An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertaint...
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Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extin...
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