An application of Hamiltonian neurodynamics using Pontryagin's Maximum (Minimum) Principle

被引：0
|
作者：
Koshizen, T [1 ]
Fulcher, J [1 ]
机构：
[1] UNIV WOLLONGONG,DEPT COMP SCI,WOLLONGONG,NSW 2522,AUSTRALIA
来源：
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS | 1995年 / 6卷 / 04期
关键词：
D O I：
10.1142/S0129065795000287
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 BackPropagation (SEP) on the XOR problem.
引用
收藏
页码：425 / 433
页数：9
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