Brown, M. and Harris, C.J.
Least Mean Square Learning in Associative Memory Networks.
Int. Symp. on Intelligent Control
Full text not available from this repository.
This paper proposes and investigates theoretically the use of a class of neural networks called Associative Memory Networks for on-line adaptive nonlinear modelling and control. This class of networks is defined to include such algorithms as the CMAC, B-splines and Fuzzy Logic. Firstly, the algorithms are defined within a unifying framework which provides a natural decomposition for a parallel implementation. Then the modelling capabilities of the networks are investigated, and some new results about the CMAC are presented. Next the instantaneous learning rules are derived and investigated from a geometrical perspective, which allows the rate of convergence to be analysed. Also a measure of the learning interference for different set (basis function) shapes is obtained. Finally, an example of an Associative Memory Network guiding an autonomous vehicle into a slot is given.
Actions (login required)