Browsing by Author "Chemaly, Timothy Paul"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemExploratory data analysis and empirical modelling of stationary processes by use of genetic programming(Stellenbosch : Stellenbosch University, 1999-12) Chemaly, Timothy Paul; Aldrich, C.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.ENGLISH SUMMARY: Enhancing the performance of any process requires a detailed knowledge of the unknown system, with a mathematical model being the most common means of representing this knowledge. The most frequently used statistical techniques, assume that any relationships between input and output variables are linear and that the data itself is normally distributed. However, real world systems can be highly non-linear and linear approaches can therefore fail to predict the behaviour of the system accurately. Explicit specification of optimal structure in large non-linear models is often not practical and as a result, non-parametric methods (kernel regression, artificial neural networks, etc.) are usually employed. Although these models allow accurate representation of complex systems, they can be very difficult to interpret. This research project explores a novel approach to this problem of mathematical modelling which attempts to evolve optimal parametric models, based on the Darwinian mechanism of evolution. This approach, referred to as genetic programming (GP), facilitates development of explicit or implicit models, or any mix of these two extremes, as dictated by the problem and unlike other methods, it can handle a trade-off between accuracy and interpretability with great ease. During this research; a -commercial application (a-GP) was developed, since very few commercial systems are currently available. Some techniques were developed, which improved the performance ofthe original algorithm considerably. For instance, memory demands were decreased by a factor of 5 by utilizing a different implementation model. Improved convergence and robustness was obtained by using a correlation-based fitness function in conjunction with a correction filter which reduced the sum of the squared errors; at the expense of a more complex model. The evaluation process was expedited by evaluating each tree-like structure as a reverse polish expression; as opposed to a branch-node reduction technique. Additional execution speed was further obtained by implementing the algorithm in c++ (an object oriented compiled language) which is significantly faster than the original LISP (an interpreted language) implementation, . The newly improved algorithm, a-GP, was applied to four industrial data sets and the results were compared against other methods such as standard genetic programming, multilayer perceptron neural networks and linear regression. It was found that a-GP outperformed standard genetic programming on all four case studies, while improving on neural networks on half of the runs. The evolved models tended to be complex. This could be attributed to the lack of parameter estimation that the genetic programming algorithm tried to compensate for by evolving complex tree structures; which it used to approximate the parameters. As a data visualization tool, a-GP was applied to four bench marking data sets used extensively in the literature. The results acquired with a-GP compared favourably with those obtained by other methods with the additional benefit in that a-GP was able to evolve simple mapping functions, which clearly indicated how the variables related to the structure. Additionally, the algorithm was applied in the mapping of two industrial processes. The results showed distinct clustering tendencies within the data, indicating the different operating regimes of the processes under investigation.