title: Comparison of four neural net learning methods for dynamic system identification.
abstract: Four types of neural net learning rules are discussed for dynamic system identification. It is shown that the feedforward network (FFN) pattern learning rule is a first-order approximation of the FFN-batch learning rule. As a result, pattern learning is valid for nonlinear activation networks provided the learning rate is small. For recurrent types of networks (RecNs), RecN-pattern learning is different from RecN-batch learning. However, the difference can be controlled by using small learning rates. While RecN-batch learning is strict in a mathematical sense, RecN-pattern learning is simple to implement and can be implemented in a real-time manner. Simulation results agree very well with the theorems derived. It is shown by simulation that for system identification problems, recurrent networks are less sensitive to noise.
authors.affiliation: Dept. of Chem. Eng., Maryland Univ., College Park, MD.
authors.forename: S Z
authors.initials: SZ
authors.lastname: Qin
authors.forename: H T
authors.initials: HT
authors.lastname: Su
authors.forename: T J
authors.initials: TJ
authors.lastname: McAvoy