Abstract
The use of neural networks in control applications has made rapid progress in recent years. In this article, two neurocontroller architectures: (a) the specialised learning, and (b) the emulator/controller are compared. Both architectures allow a neural network to learn the difficult task of balancing an inverted pendulum on a moving cart. Computer simulation results show that learning is quite rapid and both architectures take a small number of trials (15–25) for balancing the inverted pendulum.
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