| 具有强解释性的贝叶斯MA型模糊系统 |
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| 引用本文： | 顾晓清,王士同,倪彤光,蒋亦樟.具有强解释性的贝叶斯MA型模糊系统[J].控制与决策,2018,33(1):16-26. |
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| 作者姓名： | 顾晓清 王士同 倪彤光 蒋亦樟 |
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| 作者单位： | 江南大学数字媒体学院,江苏无锡214122;常州大学信息科学与工程学院,江苏常州213164,江南大学数字媒体学院,江苏无锡214122,常州大学信息科学与工程学院,江苏常州213164,江南大学数字媒体学院,江苏无锡214122 |
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| 基金项目： | 国家自然科学基金项目(61572236,61502058,61572085)；江苏省自然科学基金项目(BK 20160187)；江苏省高校自然科学基金项目(15KJB520002). |
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| 摘 要： | 提出用于规则前件学习的中心点交叉涌现的大间隔贝叶斯模糊聚类(CECLM-BFC)算法.考虑不同样本间聚类中心的排斥作用使得聚类中心间距最大化,并采用粒子滤波方法在不同类别样本中交替执行,自动求解出最优聚类结果,包括聚类数、模糊隶属度和聚类中心.在模糊规则后件参数学习上使用分类面大间隔的策略,以MA型模糊系统为研究对象构造具有强解释性的贝叶斯MA型模糊系统(BMA-FS).实验结果表明,BMA-FS能够取得令人满意的分类性能,且模糊规则具有高度的解释性.
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| 关 键 词： | 分类 粒子滤波 贝叶斯推理 Mamdani-Assilan型模糊系统 |
High interpretative Bayesian Mamdani-Assilan type fuzzy system |
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| GU Xiao-qing,WANG Shi-tong,NI Tong-guang and JIANG Yi-zhang.High interpretative Bayesian Mamdani-Assilan type fuzzy system[J].Control and Decision,2018,33(1):16-26. |
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| Authors: | GU Xiao-qing WANG Shi-tong NI Tong-guang and JIANG Yi-zhang |
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| Affiliation: | School of Digital Media,Jiangnan University,Wuxi 214122,China;School of Information Science and Technology,Changzhou University,Changzhou 213164,China,School of Digital Media,Jiangnan University,Wuxi 214122,China,School of Information Science and Technology,Changzhou University,Changzhou 213164,China and School of Digital Media,Jiangnan University,Wuxi 214122,China |
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| Abstract: | A clustering algorithm of cross-emerging-cluster large margin Bayesian fuzzy clustering(CECLM-BFC) is proposed for antecedent parameter learning. The CECLM-BFC algorithm considers repulsed force of clustering centers belonging to heterogeneous samples, and makes the maximum distances between clustering centers. A particle filter method is performed aliematively in different samples to obtain the optimal model parameters, involving the cluster numbers, fuzzy memberships and clustering centers. The learning strategy in consequent parameter learning is based on the maximum classification separation, and a Bayesian Mamdani-Assilan type fuzzy system(BMA-FS) is proposed. The experiment results show the effectiveness of the BMA-FS on classification accuracy and the number of fuzzy rules, and the obtained rules also have high interpretability. |
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