Browsing by Author "Gouws, Francois Stefan"

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    Induction of fuzzy rules from chemical process data using Growing Neural Gas and Reactive Tabu search methods
    (Stellenbosch : Stellenbosch University, 1999-10) Gouws, Francois Stefan; Aldrich, C.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: The artificial intelligence community has developed a large body of algorithms that can be employed as powerful data analysis tools. However, such tools are not readily used in petrochemical plant operational decision support. This is primarily because the models generated by such tools are either too inaccurate or too difficult to understand if of acceptable accuracy. The Combinatorial Rule Assembler (CORA) algorithm is proposed to address these problems. The algorithm uses membership functions made by the Growing Neural Gas (GNG) radial basis function network training technique to assemble internally disjunctive, Oth -order Sugeno fuzzy rules using the nongreedy Reactive Tabu Search (RTS) combinatorial search method. An evaluation of the influence of CORA training parameters revealed the following. First, for certain problems CORA models have an attribute space overlap that is one third of their GNG-generated counterparts. Second, the use of more fuzzy rules generally leads to better model accuracy. Third, decreased swap (or move) thresholds do not consistently lead to more accurate and / or simpler models. Fourth, utilisation of moves rather than swaps during rule antecedent assembly leads to better rule simplification. Fifth, consequent magnitude penalisation generally improves accuracy, especially if many rules are built. Variance of results is also usually reduced. Sixth, employing Yu rather than Zadeh operators leads to improved accuracy. Seventh, use of the GNG adjacency matrix significantly reduces the combinatorial complexity of rule construction. Eighth, AlC and BIC criteria used find the "right-sized" model exhibited local optima. Last, the CORA algorithm struggles to model problems that have a low exemplar to attribute ratio. On a chaotic time series problem the CORA algorithm builds models that are significantly (with at least 95% confidence) more accurate than those generated using multiple linear regression (MLR), CART regression trees and multivariate adaptive regression splines (MARS). However, only the RTS component models are significantly more accurate than those of the GNG and k-means (RBF) radial basis function network methods. In terms of complexity, the CORA models were significantly simpler than the CART and RBF models but more complex than the MLR, MARS and multilayer perceptron models that were evaluated. Taking all results for this problem into account, it is the author's opinion that the drop in accuracy (at worst 0.42%) of the CORA models, because of membership function merging and rule reduction, is justified by the increase in model simplicity (at least 22%). In addition, these results show that relatively intelligible "if. .. then ... " fuzzy rule models can be built from chemical process data that are competitive (in terms of accuracy) with other, less intelligible, model types (e.g. multivariate spline models).