Extension to fuzzy logic representation: Moving towards neutrosophic logic - A new laboratory rat

Real world problems have been effectively modeled using fuzzy logic that gives suitable representation of real-world data/information and enables reasoning that is approximate in nature. It is quite uncommon that the inputs captured by the fuzzy models are 100% complete and determinate. Though, humans can take intelligent decisions in such situations but fuzzy models require complete information. Incompleteness and indeterminacy in the data can arise from inherent non-linearity, time-varying nature of the process to be controlled, large unpredictable environmental disturbances, degrading sensors or other difficulties in obtaining precise and reliable measurements. Neutrosophic logic is an extended and general framework for measuring the truth, indeterminacy and falsehood-ness of the information. It is effective in representing different attributes of information like inaccuracy, incompleteness and ambiguous, thus giving fair estimate about the reliability of information. This paper suggests extending the capabilities of fuzzy representation and reasoning system by introducing Neutrosophic representation of the data and Neutrosophic reasoning system.