Abstract
In this article, a fuzzy neural network (FNN)-based approach is presented to interpret imprecise natural language (NL) commands for controlling a machine. This system, (1) interprets fuzzy linguistic information in NL commands for machines, (2) introduces a methodology to implement the contextual meaning of NL commands, and (3) recognizes machine-sensitive words from the running utterances which consist of both in-vocabulary and out-of-vocabulary words. The system achieves these capabilities through a FNN, which is used to interpret fuzzy linguistic information, a hidden Markov model-based key-word spotting system, which is used to identify machine-sensitive words among unrestricted user utterances, and a possible framework to insert the contextual meaning of words into the knowledge base employed in the fuzzy reasoning process. The system is a complete system integration which converts imprecise NL command inputs into their corresponding output signals in order to control a machine. The performance of the system specifications is examined by navigating a mobile robot in real time by unconditional speech utterances.
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