Associative Memory Network Construction Algorithms
Bridgett, N.A., Brown, M., Mills, D.J. and Harris, C.J. (1994) Associative Memory Network Construction Algorithms. Int. Symp. on Signal Processing, Robotics And Neural Networks
Full text not available from this repository.
In this paper a variety of Associative Memory Network (AMN) construction algorithms are briefly described and explained, namely the Multivariate Adaptive Regression Splines (MARS) algorithm of Friedman, and the Adaptive Spline Modelling of Observation Data (ASMOD) and Adaptive B-spline Basis function Modelling of Observation Data (ABBMOD) algorithms of Kavli. Such algorithms may be used for high-dimensional functional approximation in problems where there exits redundancy in the input data such that an accurate approximation may be formed additively from low order models. Since the process of model formation is automated, the algorithms may also be used for automatic model initialisation in some adaptive identification schemes. Since both the ASMOD and ABBMOD algorithms use B-spline basis functions which may be interpreted as a set of linguistic rules via fuzzy calculus, these algorithms may be used in the automatic generation of fuzzy rule bases.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Additional Information:||Organisation: IMACS Address: Lille, France|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
|Date Deposited:||04 May 1999|
|Last Modified:||27 Mar 2014 19:51|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)