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This paper presents a neural scheme for controlling a bus suspension system.
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Hence, although neural damage cannot be replaced by cellular proliferation, partial compensation might be provided by adaptive mechanisms, including variations in neural schemes through the unmasking of hidden neural pathways and synapses which, although not normally used, might emerge when the dominant system fails.
A robust neural control scheme for mechanical manipulators is presented.
Figure 1 Auto-associative neural networks scheme used for feature reduction.
Simulation results give superior performance of the proposed neural control scheme.
A reinforcement Q-learning optimal control approach is inserted in a batch neural fitted scheme using two neural networks to approximate the value function and the controller, respectively.
This paper presents a two-level neural network scheme for finite element (FE) model updating in which both the structural parameters and the damping ratios are updated.
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Based on evolutionary algorithms, numerical methods, neural network scheme and parallel computing technique, the optimization methodology is developed and successfully implemented.
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Three important control methods, i.e., adaptive fuzzy neural control scheme, VSS control design and H∞ tracking theory, are combined to solve the robust nonlinear output tracking problem.
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