Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies

Automated Service Identification Methods: A Review

Service identification represents the first phase in service modelling, a necessary step in SOA. This research study reviewed and analyzed the issues related to automation issues of service identification. However, the importance of service identification methods’ (SIM) automation and their business alignment are emphasized in literature, reviewing existing service identification methods (SIMs) reveals the lack of business alignment, automation as challenging issues. We close the gap by proposing ASIF which relies on automating the SIMs’ steps to identify business aligned services based on business processes and business goals

A new initialization technique in polar coordinates for Particle Swarm Optimization and Polar PSO

Particle Swarm Optimization (PSO) is one of the famous algorithms inspired by the natural behavior of a swarm (particles). However, it is used to solve n-dimensional problems in search space. One of its modified versions a Polar Particle Swarm Optimizer, was operated in polar coordinates by using an appropriate mapping function introduced based on polar coordinates. The modified algorithm faced some problems, such as generating a distorted search space, which may have been caused by the method of randomization. This paper introduces an initialization technique that operates entirely in polar coordinates. Moreover, an investigation based on standard PSO was done to test the proposed technique. The second part was to use the new initialization technique to enhance the polar PSO performance. In addition, the proposed techniques show evenly distributed points in the polar search space. Furthermore, experimental results were obtained by using various benchmark test functions on different settings of dimensions. While its shows a little enhancement in some benchmark test functions in both PSO and polar PSO, statistically there are no significant differences by using the analysis of variance (ANOVA)

Local search manoeuvres recruitment in the bees algorithm

Swarm intelligence of honey bees had motivated many bioinspired based optimisation techniques. The Bees Algorithm (BA) was created specifically by mimicking the foraging behavior of foraging bees in searching for food sources.During the searching, the original BA ignores the possibilities of the recruits being lost during the flying.The BA algorithm can become closer to the nature foraging behavior of bees by taking account of this phenomenon.This paper proposes an enhanced BA which adds a neighbourhood search parameter which we called as the Local Search Manoeuvres (LSM) recruitment factor.The parameter controls the possibilities of a bee extends its neighbourhood searching area in certain direction.The aim of LSM recruitment is to decrease the number of searching iteration in solving optimization problems that have high dimensions.The experiment results on several benchmark functions show that the BA with LSM performs better compared to the one with basic recruitment

The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier