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Internet congestion control system is complex, uncertainty and nonlinear. An Improved PID Neural Network (I-PID-NN) controller with changing integration rate and incomplete derivation in hidden layer is applied in active queue management (AQM). The adjustment of neural network parameters are implemented by using gradient algorithm as learning the rules, and the probability of packet loss can achieve...
Finite state machines have the function of reducing the complexity of the robot control system, so it's introduced into the architecture of the excavating robot. For the sake of embodying artificial intelligence and strategy in the state changing of finite state machines, BP neural network is employed in the finite state machines of the excavating robot. Aiming at the slow convergence speed of the...
In this paper, an indirect adaptive control scheme based on Takagi-Sugeno (TS)-type recurrent fuzzy models is proposed for nonlinear plants with unmeasurable states. The TS-type recurrent fuzzy model is used as the dynamic model of the nonlinear plant. Its recurrent property comes from that it can memorize temporal information with the feedback connections between its states layer and inputs layer,...
This paper presents a fuzzy neural-based filtered-X least-mean-square (LMS) algorithm for active noise control (ANC) system. The saturation of the power amplifier in ANC system is considered. A method for compensating the saturation is proposed. An on line dynamic learning algorithm based on the error gradient descent method is carried out. The convergence of the algorithm is proven using a discrete...
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