### Abstract

Original language | American English |
---|---|

Pages | 423-427 |

Number of pages | 380 |

DOIs | |

State | Published - 1 Dec 2007 |

Externally published | Yes |

Event | Electronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Proceedings - Duration: 1 Dec 2007 → … |

### Conference

Conference | Electronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Proceedings |
---|---|

Period | 1/12/07 → … |

### Fingerprint

### Cite this

*Digital representation of fuzzy inference engine*. 423-427. Paper presented at Electronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Proceedings, . https://doi.org/10.1109/CERMA.2007.4367724

}

**Digital representation of fuzzy inference engine.** / Antonio Hernández, Z.; Oscar Camacho, N.; Batyrshin, Ildar.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Digital representation of fuzzy inference engine

AU - Antonio Hernández, Z.

AU - Oscar Camacho, N.

AU - Batyrshin, Ildar

PY - 2007/12/1

Y1 - 2007/12/1

N2 - On this paper we describe steps required to fit fuzzy control into a computer code, represented with binary numbers, by using an example with two inputs and one output. This is intended because a continuous curve for the membership function is not represented at all elements; it is discretized into m quantization levels called a-levels that depend on the number of resolution bits used. Mamdani inference is applied to a pair of inputs to obtain the weights of the inferred rules using max and min operators. We have distinguished that all of defuzziftcation methods need almostk-1 iterations according to the input spaces given by 2nwhere n is the number of bits used. We will introduce a new defuzzification method called Center of Slice Area Average (COSAA), on this method, we calculate the center of area of every slice that forms resultant membership function formed by an α - level and get an average from them, requiring m-1 iterations. This defuzziftcation depends on the number of discretization levels of membership functions, not on the output space, this reduces number of instructions to be executed, in consequence fewer processing time is consumed.

AB - On this paper we describe steps required to fit fuzzy control into a computer code, represented with binary numbers, by using an example with two inputs and one output. This is intended because a continuous curve for the membership function is not represented at all elements; it is discretized into m quantization levels called a-levels that depend on the number of resolution bits used. Mamdani inference is applied to a pair of inputs to obtain the weights of the inferred rules using max and min operators. We have distinguished that all of defuzziftcation methods need almostk-1 iterations according to the input spaces given by 2nwhere n is the number of bits used. We will introduce a new defuzzification method called Center of Slice Area Average (COSAA), on this method, we calculate the center of area of every slice that forms resultant membership function formed by an α - level and get an average from them, requiring m-1 iterations. This defuzziftcation depends on the number of discretization levels of membership functions, not on the output space, this reduces number of instructions to be executed, in consequence fewer processing time is consumed.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=47349125823&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=47349125823&origin=inward

U2 - 10.1109/CERMA.2007.4367724

DO - 10.1109/CERMA.2007.4367724

M3 - Paper

SP - 423

EP - 427

ER -