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

Recovery from visual dysfunction following mild traumatic brain injury is associated with adaptive reorganization of retinal inputs to lateral geniculate nucleus in the mouse model utilizing central fluid percussion injury.

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality nationwide. Prevalence of mild TBI (mTBI) vastly outnumbers more severe forms however the associated morbidity has only recently gained public attention. Visual dysfunction is a significant component of mTBI associated morbidity with recovery of function linked with improvement in global outcomes. Examination of sensory and motor pathways in other brain injury paradigms support that recovery is largely dependent on adaptive plasticity of remaining connections. Current examinations of visual function recovery following mTBI is limited to identifying evidence for recovery and objective evidence for adaptive plasticity is limited. Therefore, to understand the mechanisms behind visual recovery in mTBI, we utilize a mouse model to examine the changes in the downstream target of retinal ganglion cells (RGC) in the formed vision pathway, the lateral geniculate nucleus (LGN). Using techniques designed to identify structural changes as well as electrophysiologic connectivity we aimed to identify if deafferentation due to experimental mTBI is met with adaptive structural and electrophysiologic reorganization of inputs to LGN relay cells, to determine if they may contribute to recovery of vision over time. Examination of ensuing deafferentation in LGN was performed using a combination of anterograde tract tracing with cholera toxin B conjugated fluorescent probes, immunohistochemistry targeting retinal ganglion cell axon terminals, and a transgenic mouse in which a subpopulation of retinal ganglion cells are labelled with green fluorescent protein. Our studies were designed to capture structural reorganization in specific subpopulations of retinal ganglion cells and determine if ensuing reorganization violated projection patterns established during normal development and refinement of the retinal geniculate pathway. Additionally, our studies examined the electrophysiologic responses of relay neurons in the lateral geniculate nucleus to stimulation of the optic tract as a function of time following injury. Using ex-vivo patch clamp recording of LGN relay neurons, we examined responses of these cells to stimulation of the optic tract following mTBI. Our findings demonstrated intact short-term depression at the retinal geniculate synapse following injury, which is a mechanism through which LGN relay neurons establish functional connectivity from retinal inputs. This innate mechanism of short-term plasticity likely uncovers latent connectivity between the remaining retinal inputs and LGN relay neurons to provide new connectivity for functional recovery. These studies support the premise that recovery of function in the visual axis following mild TBI is dependent on adaptive structural and electrophysiologic reorganization within the lateral geniculate nucleus