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Title: Symbol processing in RAAM neural networks.
Author: Day, Charles Robert.
Awarding Body: University of Keele
Current Institution: Keele University
Date of Award: 1996
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The ability to construct and manipulate recursive symbol structures is regarded as fundamentally important in the domain of cognitive modelling. The aim of this thesis dissertation is to explore how well Pollack's Recursive Auto-Associative Memory (RAAM) networks can represent and facilitate the manipulation of highly-recursive structures. Using mainly skewed and balanced binary trees, the representational power of the RAAM architecture is examined for structures which are lexically simple and syntactically complex. This is in contrast to much published work on RAAM networks, in which the structures encoded are lexically complex but syntactically simple. A new RAAM tree-processing operation, which allows partial information about a set of siblings to be used as a parent pointer, is described and tested. Several empirical investigations are motivated and carried out, to determine how effectively RAAM networks can encode highly-recursive structures. The investigations demonstrate the sensitivity of the RAAM architecture with respect to the initial conditions, training parameters and the training strategies used. This work also introduces some new techniques which help to address the twin problems of extended training times and obtaining successful RAAM encodings. A completely new method for performing terminal detection is presented as well as a technique for refining Pollack's (1990) terminal detection method. In both cases, the rate at which successful RAAM encodings are obtained is significantly better than using Pollack's method. In addition, the new implicit terminal detection method might allow improved RAAM generalisation, although this conjecture has not yet been tested.RAAM networks have been used as an important counter-example to influential analyses of the shortcomings of connectionist cognitive models. The limited success of the RAAM networks in this study brings into question connectionist hopes for an effective RAAM-based cognitive model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer software & programming Computer software Bionics