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The high precision of a piezo-electric positioning stage almost depends on whether the designed controller can effectively compensate the inherent hysteresis phenomenon. In this paper, an adaptive output feedback controller based on a radial basis function neural network (RBFNN) is proposed to eliminate the tracking errors caused by the hysteresis behavior. The observer-based RBFNN is used to online...
A neural network (NN)-based backstepping adaptive control is proposed for the stabilized platform system of shipborne weapons (SPOSW) with uncertain nonlinear friction torque and load disturbance. First, with the using of NN approximator, an equivalent model is developed. Where the NN is used to estimate the uncertain nonlinear friction torque. Then, based on the equivalent model, address the problem...
The complex dynamics behaviors of the Rossler reaction systems are studied in the paper. By applying bifurcation diagram and phase diagram are presented to analyze periodic oscillation state and chaos motions. Periodic and chaotic motions of the system can be distinguished by Lyapunov exponent method. The chaotic motion of the system is controlled using neural network controller. We obtain the steady...
Unauthorized network address translation (NAT) devices may be a significant security problem. They provide unrestricted access to any number of hosts connecting to them. Some attackers may use computers hidden behind NAT devices to conduct malicious activities such as denial of service. An algorithm is proposed in this work to detect hosts hidden behind NAT. Different from previous researches, the...
This paper deals with load optimal dispatching among power plant units in the context of electric power market competition environment. The multi-agent evolutionary algorithm (MAEA) is specially useful in load optimal dispatching, the model of which is set up based on characteristic curve of the generator coal consumption approached by quadratic function. In particular, the following restraints are...
Based on the demands for logistics services, establish a dynamic neural network for quality function deployment, and combined with an automotive logistics enterprise, build a linear programming model for evaluation. The optimization method this paper presents will make logistics enterprises get the market demand momentarily and optimize the indexes possibly.
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