Abstract for robinson_phd

Cambridge University PhD Thesis


Anthony J. Robinson

February 1989

This thesis extends the error propagation network to deal with time varying or dynamic patterns. Examples are given of supervised, reinforcement driven and unsupervised learning.

Chapter 1 presents an overview of connectionist models.

Chapter 2 introduces the error propagation algorithm for general node types.

Chapter 3 discusses the issue of data representation in connectionist models.

Chapter 4 describes the use of several types of networks applied to the problem of the recognition of steady state vowels from multiple speakers.

Chapter 5 extends the error propagation algorithm to deal with time varying input. Three possible architectures are explored which deal with learning sequences of known length and sequences of unknown and possibly indefinite length. Several simple examples are given.

Chapter 6 describes the use of two dynamic nets to form a speech coder. The popular method of Differential Pulse Code Modulation for speech coding employs two linear filters to encode and decode speech. By generalising these to non-linear filters, implemented as dynamic nets, a reduction in the noise imposed by a limited bandwidth channel is achieved.

Chapter 7 describes the application of a dynamic net to the recognition of a large subset of the phonemes of English from continuous speech. The dynamic net is found to give a higher recognition rate both in comparison with a fixed window net and with the established k nearest neighbour technique.

Chapter 8 describes a further development of dynamic nets which allows them to be trained by a reinforcement signal which expresses the correctness of the output of the net. Two possible architectures are given and an example of learning to play the game of noughts and crosses is presented.

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