Neural networks have been shown to give considerably better results when controlling complex non-linear systems than conventional control methods. Most neural network controllers today are built around `passive' learning methods whereby the network once trained is expected to perform repeatedly with equal accuracy on fresh sets of input-output data. This is not always suitable in real world situations where external environmental parameter variations cause changes in the plant and controller performance. In the current paper we propose the use of an autonomous `active' learning technique which will cause training to re-occur precisely when these parameter variations happen, yielding enhanced controller performance.
|Title of host publication||Control 95 : meeting the challenge of Asia Pacific growth|
|Place of Publication||Barton|
|Publisher||Institute of Engineers Australia|
|Number of pages||5|
|Publication status||Published - 1995|