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A Synthesis of Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/91

A Synthesis of Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks

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Title: A Synthesis of Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks
Author: Howse, J.W.; Abdallah, C.T.; Heileman, G.L.
Subject(s): Dynamical systems
System Identification
Abstract: The process of model learning can be considered in two stages: model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n - 1) Hamiltonian systems. Thus, the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented. This algorithm is proven to converge to the desired system trajectory for all initial conditions and system inputs. This technique can be used to design neural network models which are guaranteed to solve certain classes of nonlinear identification problems.
Date: 1995-07-27
Series: EECE-TR-95-003
Description: Technical Report
URI: http://hdl.handle.net/1928/91

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