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Method of Using Data from Intelligent Machine Short-Term Memory in Fuzzy Logic System

Decision making based on the process’s history is the main challenge of the Internet of Things (IoT). Spatiotemporal datasets from a lot of heterogeneous sensors represent process’s history. Fuzzy logic systems (FLS) are not used in such conditions due to the large dimension of the problem. The two-stage Computing with Words (CWW) approach overcomes the dimensionality problem by using abstraction engine at the first stage, which maps the meaning of data from a lot of sensors to the meaning of a few words. In this article the CWW technology is extended by a Short-Term Memory (STM) model that stores a time sequence of data in the form of an ordered sequence of events. Fuzzy characteristics of words of a high level of abstraction represent the events. The STM model is supported by the footprint blur algorithm, in which calculations are performed considering the event aging rate. The preprocessing and postprocessing data models are presented, what allows integrate the STM model with FLS and using it for making decisions in IoT. An example of the use of STM data in the FLS when making decisions by an intelligent machine is given.