A New Modular Strategy For Action Sequence Automation Using Neural Networks And Hidden Markov Models

Mohamed Adel Taher and Mostapha Abdeljawad
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Mohamed Adel Taher: Marine Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Mostapha Abdeljawad: Marine Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt

International Journal of System Dynamics Applications (IJSDA), 2013, vol. 2, issue 3, 18-35

Abstract: In this paper, the authors propose a new hybrid strategy (using artificial neural networks and hidden Markov models) for skill automation. The strategy is based on the concept of using an “adaptive desired” that is introduced in the paper. The authors explain how using an adaptive desired can help a system for which an explicit model is not available or is difficult to obtain to smartly cope with environmental disturbances without requiring explicit rules specification (as with fuzzy systems). At the same time, unlike the currently available hidden Markov-based systems, the system does not merely replay a memorized skill. Instead, it takes into account the current system state as reported by sensors. The authors approach can be considered a bridge between the spirit of conventional automatic control theory and fuzzy/hidden Markov-based thinking. To demonstrate the different aspects of the proposed strategy, the authors discuss its application to underwater welding automation.

Date: 2013
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