Abstract
A neural network approach is presented for the adaptive control of real-time systems. Forward modeling and the partial inversion algorithm are used to solve the one-to-many mapping problem in constructing a neural controller. Inputs are disaggregated into controllable and uncontrollable inputs, and an algorithm partially inverting the network is used to control the system where only controllable inputs are adjusted based on the gradient of a control error. The suggested neural network scheme is applied to a traffic signal control system. The results show the effectiveness of the approach and suggest the potential applications to the real-time systems such as manufacturing control system, process control system, and communication network system.
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