Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure

Aesthetic Automata: Synthesis and Simulation of Aesthetic Behaviour in Cellular Automata

This thesis addresses the computational notion of aesthetics in the framework of multistate two-dimensional cellular automata (2D CA). The measure of complexity is a core concept in computational approaches to aesthetics. Shannon's information theory provided an objective measure of complexity, which led to the emergence of various informational theories of aesthetics. However, entropy fails to take into account the spatial characteristics of 2D patterns; these characteristics are fundamental in addressing the aesthetic problem, in general, and of CA-generated patterns, in particular. This thesis proposes two empirically evaluated alternative measures of complexity, taking into account the spatial characteristics of 2D patterns and experimental studies on human aesthetic perception in the visual domain. The measures are extended to robustly quantify the complexity of multi-state 2D CA-generated patterns. The first model, spatial complexity, is based on the probabilistic spatial distribution of homogeneous/heterogeneous neighbouring cells over the lattice of a multi-state 2D cellular automaton. The second model is based on algorithmic information theory (Kolmogorov complexity) which is extended to estimate the complexity of 2D patterns. The spatial complexity measure presents performance advantage over information-theoretic models, specifically in discriminating symmetries and the orientation in CA-generated patterns, enabling more accurate measurement of complexity in relation to aesthetic evaluations of 2D patterns. A series of experimental stimuli with various structural characteristics and levels of complexity were generated by seeding 3-state 2D CA with different initial configurations for psychological experiments. The results of experimentation demonstrate the presence of correlation between spatial complexity measures and aesthetic judgements of experimental stimuli. The same results were obtained for the estimations of Kolmogorov complexity of experimental stimuli

Condition Monitoring of Machine Tool Ball Screw Feed Drives Through Signal Analysis and Artificial Intelligence

This thesis is set in the context of the large volume of work directed to improving the overall equipment effectiveness (OEE) of manufacturing machines. Of the three OEE factors, performance receives much research attention since it provides simple metrics of parts per hour produced. However, availability and quality, which are the other two factors, can play an equally important role. In the past, high availability has been achieved by time-based or preventive maintenance techniques, which can be expensive and wasteful due to needless repairs and replacement of useful parts. This research aims to develop a cost-effective strategy for machine tool maintenance that improves availability and accuracy by adopting a condition-based or predictive maintenance approach. The approaches under investigation use both machine learning and deep learning techniques to analyse continuous time-series signals to assess a machine tool's condition. For this research, the focus is on applying the techniques to the ball screw assembly of axis feed drives. This is one of the most common machine tool parts whose degradation can affect its availability and positional accuracy. This research data is obtained from experiments on a gantry-type machine tool with two ball screws, where one is good, and the other is worn. In the machine learning approach, wavelet and fast Fourier transforms are employed for data processing on time-series vibration readings before extracting useful features for model training. These extracted features consistently show better accuracy across several machine learning algorithms than those obtained via classical methods. Deep learning is then investigated as an alternative method of analysing time-series data. The chosen approach utilises a pre-trained deep learning neural network based on convolution, which had been successfully used to learn from image files. The novelty in this research arises from the use of convolution-based deep learning on time series data. It does this by the conversion of the vibration signals to image files. The method of converting time-series data streams to images relevant for this analysis has been established and verified. Test results show that the wavelet and fast Fourier transform (FFT) features used in the machine learning approach can outperform the statistical features in classifying the condition of the ball screw. With at least a 98 % accuracy across the examined machine learning networks compared to a range of 87 % (support vector machine) to 96 % (k nearest neighbour). On the other hand, the deep learning technique can achieve at least 98 % or 100 % accuracy when trained with raw and processed data, respectively. The deep learning approach has the advantage of requiring less data processing and better accuracy than the machine learning approach. This research project will contribute to the manufacturing industry by improving the overall equipment effectiveness at a low cost. Furthermore, it can lead to real-time online condition monitoring with less overhead since there is no need for a data processing stage. This research's natural progression would be applying this approach to other parts of a machine tool or equipment. Furthermore, investigating and identifying specific faults and their progression would lead to a more sophisticated system for widespread deployment