A neurocomputer based learning controller for critical industrial applications

Because of their remarkable ability to learn nonlinear mappings from a nonexhaustive training set and generalize, neurocomputers can readily play the role of learning controllers for complex plants. Due to the parallel distributed nature of processing they introduce little time delay in the control loop. In this paper a new generalized feedforward neural network model is applied to the task of controlling an aircraft engine model. A more effective learning algorithm based on adaptive optimization is presented which compares favorably with the classical backpropagation method. Similar controllers can be designed for critical industrial applications such as nuclear power plants etc.<
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