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I assume that the reader of this thesis is reasonably familiar with artificial neural network (ANN) methods in computer science, including the multi-layer perceptron (ML?) and the backpropagation training method. I have not needed to use any difficult or esoteric mathematics; the major mathematical concept encountered in the thesis is the multiset (which is easy to grasp for anyone familiar with set theory). Certain chapters also make use of the notions of partial derivatives. inner products in arbitrary vector spaces, and metrics.

This thesis investigates the use of self-adaptation and neural constructivism within a neural Learning Classifier System framework. The system uses a classifier structure whereby each classifier condition is represented by an artificial neural network, which is used to compute an action in response to an environmental stimulus. We implement this neural representation in two modem Learning Classifier Systems, XCS and XCSF. A classic problem in neural networks revolves around network topology considerations; how many neurons should the network consist of? How should we configure their topological arrangement and inter-neural connectivity patterns to ensure high performance? Similarly in Learning Classifier Systems, hand-tuning of parameters is sometimes necessary to achieve acceptable system performance. We employ a number of mechanisms to address these potential deficiencies. Neural Constructivism is utilised to automatically alter network topology to reflect the complexity of the environment. It is shown that appropriate internal classifier complexity emerges during learning at a rate controlled by the learner. The resulting systems are applied to real-valued, noisy simulated maze environments and a simulated robotics platform. The main areas of novelty include the first use of self-adaptive constructivism within XCSF, the first implementation of temporally-sensitive spiking classifier representations within this constructive XC SF, and the demonstration of temporal functionality of such representations in noisy continuous-valued and robotic environments.

The research described in this thesis presents a new classification technique for continuous electroencephalographic (EEG) recordings, based on a network of spiking neurons. Analysis of the signals is performed on ensemble EEG and the task of the neural network is to identify the P300 component in the signals. The network employs leaky-integrate-and-fire neurons as nodes in a multi-layered structure. The method involves formation of multiple weak classifiers to perform voting and collective results are used for final classification.

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The work presented in this thesis is mainly involved in the study of Artificial Neural Networks (ANNs) and their learning strategies. The ANN simulator incorporating the Backpropagation (BP) algorithm is designed and analysed and run on a MIMD parallel computer namely the Balance 8000 multiprocessor machine. Initially, an overview of the learning algorithms of ANNs are described. Some of the acceleration techniques including Heuristic methods for the BP like algorithms are introduced. The software design of the simulator for both On-line and Batch BP is described. Two different strategies for parallelism are considered and the results of the speedups of both algorithms are compared. Later a Heuristic algorithm (GRBH) for accelerating the BP method is introduced and the results are compared with the BP using a variety of expositing examples. The simulator is used to train networks for invariant character recognition using moments. The trained networks are tested for different examples and the results are analysed. The thesis concludes with a chapter summarizing the main results and suggestions for further study.

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The objective of this study was to create a market prediction model for small and medium enterprises (SMEs). To achieve this, an extensive literature examination was carried out which focused on SMEs, marketing and prediction; neural networks as a competitive tool for SME marketing; and clustering a review. A Delphi study was used for collating expert opinions in order to determine likely factors hindering SMEs wanting to remain business proficient. An analysis of Delphi responses led to the creation of a market prediction questionnaire. This questionnaire was used to create variables for analysis using four unsupervised algorithm. The algorithms used in this study were joining tree, k-means, learning vector quantisation and the snap-drift algorithm. Questionnaire data took the form of data collected from 102 SMEs. This led to the determination of 23 variables that could best represent the data under examination. Further analysis of each 23 variable led to the choice of respondents for case study analysis. A higher education college (HEC) and a private hire company (PHC) were chosen for this stage of the research. In case study one (1), analysis has discovered that HEC's can compete with Universities if they tailor their products and services to selected academic markets as opposed to entering all academic sectors. The findings suggest that if a HEC monitors the growth of its students and establishes the likely point of creating new courses they will retain students and not lose them to universities. Comparisons between the case HEC and rival HECs has demonstrated that there is a knowledge gap that currently exists between these institutions and by using post-modem marketing coupled with neural networks a competitive advantage will be realised. In case study two (2), a private hire company was investigated allowing for the interpretation of current markets for this firm by making existing operating areas more transparent. Therefore, knowledge barriers were discovered between telephonists and drivers, and the owner/manger and drivers. As such historical data was used for distinguishing the performance of drivers within this firm. In differentiating job times and driver performance our case organisation was better equipped for determining the times in which it is most busy. Therefore, being able to determine the amount of telephonists needed per shift and the likely busy periods in which this firm will operate. Analysis of all participating SMEs have revealed that: (1) these firms are more likely to fail in the first two years of operation generally, (2) successful SMEs are owned or managed by persons having prior management and or general business expertise, (3) success is normally attributed to experience gained as a result of working or managing a threatened firm in the past, (4) successful SMEs understand the importance of valuing the ethnicity held in their respective firms and (5) these firms are less likely to understand how technology can aid and sustain market growth generally. It seems market prediction in SMEs can be affected by employee performance and managerial ability to undertake predefined tasks. The findings suggest that there are SMEs that can benefit from market prediction. More importantly, the findings indicate the need to understand the SME for determining the types of intelligent systems that can be used for initiate marketing and providing marketing prediction generally. Several theoretical and practical implications are discussed. To this effect, SME owner/managers, researchers in academia, government and public SME organisations can learn from the results. Suggestions for future research are also presented.

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