Neural-network based adaptive control of uncertain chaotic systems

In this paper, a new adaptive control scheme, that employs a Gaussian radial basis function network with output weights updated on-line, is developed for regulating a class of uncertain chaotic systems. Theoretical analysis guarantees that the controlled uncertain chaotic systems can asymptotically track target orbits within arbitrarily small tolerance bounds. The uncertain Duffing-Holmes system is used as an example for illustration.