Intelligent methods for pattern recognition and optimisation

This dissertation presents and discusses the processes of investigation, implementation, testing, validation and evaluation of several computational intelligence-based systems for solving four large-scale real-world problems. In particular, two industrial problems from the pattern recognition and two from the process optimisation areas are studied and intelligent methods to address them are proposed, developed and tested using real-world data. The first problem investigated is the application of an intelligent visual inspection system for classification of texture images. Two major approaches, incorporating supervised and unsupervised (without a priori knowledge) learning techniques, are considered and neural network based classifiers are trained. The focus is kept on the application of unsupervised non-linear dimensionality reduction techniques in combination with unsupervised classification methods. A number of experiments and simulations are performed to evaluate the proposed approaches and the results are critically compared. Next, a classification problem for timely and reliable identification of emitters of radar signals is investigated. A large data set, containing a considerable amount of missing data is used. Several techniques for dealing with the incomplete data values are employed, including listwise deletion and multiple imputation. Methods incorporating neural network classifiers are studied and the proposed approaches are tested and validated over a number of simulations in the MATLAB environment. The third large-scale problem, presented in this work, addresses the need for optimisation of a thermodynamics first principle-based prediction model for simulation of a major purifying process, used in British Petroleum (BP) refineries. A technique incorporating genetic algorithms is applied for optimising a number of the model parameters and for closing up the gaps between the predicted and measured data. Several functions and a graphical user interface (GUI) tool are implemented in MATLAB to assist the analysis, optimisation, testing and validation of the investigated model. Significant overall improvement in its prediction capabilities is achieved. The final problem, covered in this research work, is the need to improve the convergence rate of a computationally very expensive aerodynamic optimisation process. It is addressed by exploring some physics-grounded heuristics and presenting a novel intelligent approach for automated shape optimisation. A set of basis functions (for spanning the design space) is derived in such a way that they facilitate the work of a time-consuming and expensive computational fluid dynamics (CFD) optimisation process. Two MATLAB-based GUI tools are developed to support the calculation, exploration, testing and validation of the studied approach. Experiments for optimising real aircraft geometry are run on supercomputers through an industrial partner (AIRBUS Operations Ltd). The initial results show very promising opportunities for improving the convergence rate of the slow optimisation process.