Training of Neural Networks Using the Smooth Variable Structure Filter with Application to Fault Detection

Abstract

Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed data. The training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local minima. In this thesis, a novel training method is proposed. This method is based on the relatively new Smooth Variable Structure filter (SVSF) and is formulated for feedforward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the Sliding Mode Concept and works in a predictor-corrector fashion. The SVSF applies a discontinuous corrective term to estimate state and parameters. Its advantages include guaranteed stability, robustness, and fast speed of convergence. The proposed training technique is applied to three real-world benchmark problems and to a fault detection application in a Ford diesel engine. SVSF-based training technique shows an excellent generalization capability and a fast speed of convergence.

Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed data. The training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local minima. In this thesis, a novel training method is proposed. This method is based on the relatively new Smooth Variable Structure filter (SVSF) and is formulated for feedforward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the Sliding Mode Concept and works in a predictor-corrector fashion. The SVSF applies a discontinuous corrective term to estimate state and parameters. Its advantages include guaranteed stability, robustness, and fast speed of convergence. The proposed training technique is applied to three real-world benchmark problems and to a fault detection application in a Ford diesel engine. SVSF-based training technique shows an excellent generalization capability and a fast speed of convergence.