neurocontrollers; training data; neural network; evolutionary computation
One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process are used to construct a training set for the NN. The NN leams the training set, is able to generalize to new plant states, and is then used for neurocontrol. Thus the EC/NN approach combines the broad, parallel search of EC with the rapid execution and generalization of NN to produce a viable solution to the control problem. This paper presents the ECNN hybrid and demonstrates its utility in developing a neurocontroller that demonstrates stability, generalization, and optimality.
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