### Abstract

The role of the a priori probabilities is unclear for a stochastic fuzzy control with a single vector data described in the first report, because in such a case the number of Gaussian potential functions is equal to the number of control rules. In addition, as in a conventional fuzzy control based on fuzzy reasoning, it is necessary to clarify the relationships between the fuzzy sets and the control rules. We here consider a static multiple model adaptive control (MMAC) for the initial data distribution, in which it is assumed that a single vector data is partitioned into two subvector data and the hierarchical hypotheses are introduced with respect to each initial data distribution. Then, two kinds of stochastic fuzzy control are proposed on the basis of such a static MMAC: one case assigns the a priori probabilities to the fuzzy sets for each subvector data, and the other case assigns the a priori probabilities to the control rules.

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

Pages (from-to) | 1013-1018 |

Number of pages | 6 |

Journal | Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C |

Volume | 62 |

Issue number | 595 |

Publication status | Published - Mar 1996 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Mechanical Engineering

### Cite this

**Stochastic fuzzy control (2nd report, Relationships among a priori probabilities, fuzzy sets and control rules).** / Watanabe, Keigo.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Stochastic fuzzy control (2nd report, Relationships among a priori probabilities, fuzzy sets and control rules)

AU - Watanabe, Keigo

PY - 1996/3

Y1 - 1996/3

N2 - The role of the a priori probabilities is unclear for a stochastic fuzzy control with a single vector data described in the first report, because in such a case the number of Gaussian potential functions is equal to the number of control rules. In addition, as in a conventional fuzzy control based on fuzzy reasoning, it is necessary to clarify the relationships between the fuzzy sets and the control rules. We here consider a static multiple model adaptive control (MMAC) for the initial data distribution, in which it is assumed that a single vector data is partitioned into two subvector data and the hierarchical hypotheses are introduced with respect to each initial data distribution. Then, two kinds of stochastic fuzzy control are proposed on the basis of such a static MMAC: one case assigns the a priori probabilities to the fuzzy sets for each subvector data, and the other case assigns the a priori probabilities to the control rules.

AB - The role of the a priori probabilities is unclear for a stochastic fuzzy control with a single vector data described in the first report, because in such a case the number of Gaussian potential functions is equal to the number of control rules. In addition, as in a conventional fuzzy control based on fuzzy reasoning, it is necessary to clarify the relationships between the fuzzy sets and the control rules. We here consider a static multiple model adaptive control (MMAC) for the initial data distribution, in which it is assumed that a single vector data is partitioned into two subvector data and the hierarchical hypotheses are introduced with respect to each initial data distribution. Then, two kinds of stochastic fuzzy control are proposed on the basis of such a static MMAC: one case assigns the a priori probabilities to the fuzzy sets for each subvector data, and the other case assigns the a priori probabilities to the control rules.

UR - http://www.scopus.com/inward/record.url?scp=0030102571&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030102571&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0030102571

VL - 62

SP - 1013

EP - 1018

JO - Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C

JF - Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C

SN - 0387-5024

IS - 595

ER -