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Title: Ameliorating integrated sensor drift and imperfections : an adaptive 'neural' approach
Author: Tang, Tong Boon
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2006
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This thesis examines the suggestion that local pre-processing and early classification of high-dimensional sensory signals can be achieved effectively by an artificial neural network (ANN). The multisensor microsystem for a project named “Integrated Diagnostics for Environmental & Analytical Systems (IDEAS)” is used as an example for this study. Four types of electrochemical sensors are implemented and calibrated. In our testbench experiments, the sensory signals are found to experience some stochastic randomness and drift during operation. Therefore, the ANN must be adaptive to allow auto-calibration of the sensors. This thesis reviews existing ANN algorithms to fuse sensory signals and identifies hardware-amenable neural algorithms. The Continuous Restricted Boltzmann machine (CRBM) is chosen as a suitable candidate. The CRBM is further developed in this thesis to facilitate online learning without experiencing Catastrophic Interference (CI) - a known problem in associative memory based models. The CRBM is examined in two separate simulations. The first simulation evaluates the modelling capability of the CRBM while the second simulation focuses on the adaptation of the CRBM to sensor drift in a dynamic environment. The results suggest that the CRBM is able to model high-dimensional, non-Gaussian data distributions with overlapping areas. The CRBM is also compared favourably, in terms of robustness against sensor drift, with trained but subsequently non-adaptive neural models. The thesis also investigates the optimal architecture size and learning rate for the CRBM. Finally, the limitations of the CRBM are studied. The learning rate is identified as the key factor in determining the feasibility of CRBM tracking sensor drift in a dynamic environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available