title: An application of Hamiltonian neurodynamics using Pontryagin's Maximum (Minimum) Principle.
abstract: Classical optimal control methods, notably Pontryagin's Maximum (Minimum) Principle (PMP) can be employed, together with Hamiltonians, to determine optimal system weights in Artificial Neural dynamical systems. A new learning rule based on weight equations derived using PMP is shown to be suitable for both discrete- and continuous-time systems, and moreover, can also be applied to feedback networks. Preliminary testing shows that this PMP learning rule compares favorably with Standard BackPropagations (SBP) on the XOR problem.
authors.affiliation: Department of Computer Science, University of Wollongong, NSW, Australia.
authors.forename: T
authors.initials: T
authors.lastname: Koshizen
authors.forename: J
authors.initials: J
authors.lastname: Fulcher
mesh_terms: D008962:Models, Theoretical; D016571:Neural Networks, Computer