Abstract
The Bounded Derivative Network (BDN), the analytical integral of a neural network, is a natural and elegant evolution of universal approximating technology for use in automatic control schemes. This modeling approach circumvents the many real problems associated with standard neural networks in control such as model saturation (zero gain), arbitrary model gain inversion, ‘black box’ representation and inability to interpolate sensibly in regions of sparse excitation. Although extrapolation is typically not an advantage unless the understanding of the process is complete, the BDN can incorporate process knowledge in order that its extrapolation capability is inherently sensible in areas of data sparsity. This ability to impart process knowledge on the BDN model enables it to be safely incorporated into a model based control scheme.
Published Version
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