Abstract
This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.
Highlights
It is well known that fuzzy inference models are very important in applications when information is uncertain and imprecise, like: robotics, medicine, control, modeling, and so forth [1,2,3,4,5,6]
The fuzzy-molecular position controller for a direct current (DC) motor described in Section 5 was reduced to a type1 fuzzy system by only considering the primary membership functions in the fuzzification step, as well as in the Mamdani’s fuzzy controller
A new fuzzy algorithm based on artificial hydrocarbon networks called fuzzy-molecular inference model (FMI-model) was proposed, taking advantage of the power of molecular units of information
Summary
It is well known that fuzzy inference models are very important in applications when information is uncertain and imprecise, like: robotics, medicine, control, modeling, and so forth [1,2,3,4,5,6]. The literature reports three main models: Takagi-Sugeno inference systems [7], Mamdani’s fuzzy control systems [8], and Tsukamoto’s inference model [9]. Takagi-Sugeno inference systems apply polynomial functions to construct the consequent values using pairs of input-output data of a given system to model [7]. Mamdani’s fuzzy control systems refer to control laws that apply fuzzy inference models with fuzzy partitions in the defuzzification phase [8], obtaining mostly the output value with the center of gravity (COG) function [10].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
