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An Investigation Into The Use Of Neural Networks For The Prediction Of The Stock Exchange Of Thailand, Suchira Chaigusin
An Investigation Into The Use Of Neural Networks For The Prediction Of The Stock Exchange Of Thailand, Suchira Chaigusin
Theses: Doctorates and Masters
Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to be studied extensively and research focused on the SET will contribute to understanding its unique characteristics and will lead to identifying …
A Generalised Feedforward Neural Network Architecture And Its Applications To Classification And Regression, Ganesh Arulampalam
A Generalised Feedforward Neural Network Architecture And Its Applications To Classification And Regression, Ganesh Arulampalam
Theses: Doctorates and Masters
Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification …
Contrast Enhancement Of Ultrasound Images Using Shunting Inhibitory Cellular Neural Networks, Murali M. Gogineni
Contrast Enhancement Of Ultrasound Images Using Shunting Inhibitory Cellular Neural Networks, Murali M. Gogineni
Theses: Doctorates and Masters
Evolving from neuro-biological insights, neural network technology gives a computer system an amazing capacity to actually generate decisions dynamically. However, as the amount of data to be processed increases, there is a demand for developing new types of networks such as Cellular Neural Networks (CNN), to ease the computational burden without compromising the outcomes. The objective of this thesis is to research the capability of Shunting Inhibitory Cellular Neural Networks (SICNN) to solve the clarity problems in ultrasound imaging. In this thesis, we begin by reviewing a number of traditional enhancement techniques and measures. Since the entire work of this …
An Adaptive Hierarchical Fuzzy Logic System For Modelling And Prediction Of Financial Systems, Mark Kingham
An Adaptive Hierarchical Fuzzy Logic System For Modelling And Prediction Of Financial Systems, Mark Kingham
Theses: Doctorates and Masters
In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to …
A Model Of Visual Recognition Implemented Using Neural Networks, Vincent C. Phillips
A Model Of Visual Recognition Implemented Using Neural Networks, Vincent C. Phillips
Theses: Doctorates and Masters
The ability to recognise and classify objects in the environment is an important property of biological vision. It is highly desirable that artificial vision systems also have this ability. This thesis documents research into the use of artificial neural networks to implement a prototype model of visual object recognition. The prototype model, describing a computtional architecture, is derived from relevant physiological and psychological data, and attempts to resolve the use of structural decomposition and invariant feature detection. To validate the research a partial implementation of the model has been constructed using multiple neural networks. A linear feed-forward network performs pre-procesing …
Applications Of Fuzzy Counterpropagation Neural Networks To Non-Linear Function Approximation And Background Noise Elimination, I. M. Wiryana
Applications Of Fuzzy Counterpropagation Neural Networks To Non-Linear Function Approximation And Background Noise Elimination, I. M. Wiryana
Theses: Doctorates and Masters
An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem …