Xiao-gang Ruan

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Aiming at the problem about the movement balance of two-wheeled self-balancing mobile robot, a learning mechanism of the operant conditioning theory based on recurrent neural network is used. The critical function is approached and the most superior choice to the action is made by recurrent neural network. Thus, the two-wheeled self-balancing mobile robot(More)
Inverted pendulum is a control system, with the feature of high order, muti-variable, non-linearity and unstable naturally. It is quite important for us to study its balance and stability in control engineering field. In this paper, a flywheel inverted pendulum, as an object to be controlled, the dynamic model has been established, and the mathematical(More)
Aiming at the problem about the posture balance control of two-wheeled self-balancing mobile robot, a learning algorithm that it is made up of BP neural network and eligibility traces based on the operant conditioning theory is put forward as a learning mechanism of the two-wheeled robot. The algorithm utilizes the characters of eligibility traces about(More)
—Aiming at the movement balance problem of the two-wheeled robot, the operant conditioning theory of artificial cerebellar sensorimotor systems is used, and the theory adopts a learning mechanisms of Skinner's operation conditioned based on the learning algorithm of recurrent neural network, through learning and training, the two-wheeled robot can obtain(More)
A modified difference Hopfield neural network is proposed to overcome the multiple local minimum problem of normal difference Hopfield neural network. On conditions that the modified Hopfield neural network works in a parallel mode and its interconnection weight matrix is negative, it has only one stable state, and the stable state can make its energy(More)
The cerebellum has long been thought to play a crucial role in the forming of graceful movements, and it is viewed as a set of modules, each of which can be added to a control system to improve smooth coordinated movement, with improvements continuing and improving over time. The present paper combines the microcomplex view of the cerebellum's role in motor(More)
In this paper, a MISO fuzzy neural network algorithm is presented. This algorithm consists of the excellences of fuzzy algorithm and neural network algorithm. In the parameter learning phase it changes the parameters based on the Lyapunov stability theory to ensure the stability. Meanwhile, it didnpsilat need to seek the whole minimum value when it modifies(More)
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