Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression

Loading...
Thumbnail Image
Date
2010-05
Authors
Yap, Keem Siah
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research. The resulting hybrid neural network model (denoted as GART) is capable of handling pattern classification and data regression problems. The capability of GART is further enhanced (denoted as EGART) with a number of features, which include the used of Laplacian loss and likelihood functions, a new definition of vigilance function, a match tracking mechanism. In addition, a pre-processing technique, i.e., the ordering algorithm, for determining the presentation sequence of training samples is applied (denoted as O-EGART). The O-EGART model is equipped with a series of postprocessing procedures (denoted as O-EGART-PR), i.e., network pruning and rule extraction. Network pruning requires computation of the confidence factor of each protoptye node in O-EGART-PR based on a set of validation samples. A quantization process is also applied to convert the prototype weights into a set of IF-THEN rules. In addition, a standard Fuzzy Inference System (FIS) is constructed (denoted as OEGART- PR-FIS) in order to evaluate the quality of the extracted rules. The performances of the proposed ART-based models are compared with those from other approaches using benchmark data sets, and the bootstrap method is used to quantify the results. To evaluate the practical applicability of the proposed ART-based models, empirical experiments based on seven benchmark and real-world data sets, i.e., three from power systems, three from fire safety engineering, and one from medical application, are conducted. These results show good performances, e.g., accuracy rates are 98.92% and 97.20% for classification of harmonic currents in distribution network and diagnosis of circulating water systems in power generation plant, respectively, hence justified the usefulness of the proposed ART-based models in undertaking pattern classification and data regression problems.
Description
Keywords
Novel neural network models , for tackling pattern classification
Citation