ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 知识嵌入的贝叶斯MA型模糊系统

1. 1(江南大学数字媒体学院 江苏无锡 214122); 2(常州大学信息科学与工程学院 江苏常州 213164) (czxqgu@163.com)
• 出版日期: 2017-05-01
• 基金资助:
国家自然科学基金项目(61572236,61502058,61572085);江苏省自然科学基金项目(BK20160187);中央高校基本科研业务费专项资金项目(JUSRP51614A);江苏省高校自然科学基金项目(15KJB520002)

### Knowledge Embedded Bayesian MA Fuzzy System

Gu Xiaoqing1,2, Wang Shitong1

1. 1(School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122); 2(School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164)
• Online: 2017-05-01

Abstract: The most distinctive characteristic of fuzzy system is its high interpretability. But the fuzzy rules obtained by classical cluster based fuzzy systems commonly need to cover all features of input space and often overlap each other. Specially, when facing the high-dimension problem, the fuzzy rules often become more sophisticated because of too much features involved in antecedent parameters. In order to overcome these shortcomings, based on the Bayesian inference framework, knowledge embedded Bayesian Mamdan-Assilan type fuzzy system (KE-B-MA) is proposed by focusing on the Mamdan-Assilan (MA) type fuzzy system. First, the DC (dont care) approach is incorporated into the selection of fuzzy membership centers and features of input space. Second, in order to enhance the classification performance of obtained fuzzy systems, KE-B-MA learns both antecedent and consequent parameter of fuzzy rules simultaneously by a Markov chain Monte Carlo (MCMC) method, and the obtained parameters can be guaranteed to be global optimal solutions. The experimental results on a synthetic dataset and several UCI machine datasets show that the classification accuracy of KE-B-MA is comparable to several classical fuzzy systems with distinctive ability of providing explicit knowledge in the form of interpretable fuzzy rules. Rather than being rivals, fuzziness in KE-B-MA and probability can be well incorporated.