有混合数据输入的自适应模糊神经推理系统

张宇献; 郭佳强; 钱小毅; 王建辉

doi:10.16383/j.aas.2018.c170698

有混合数据输入的自适应模糊神经推理系统

doi: 10.16383/j.aas.2018.c170698

1.
沈阳工业大学电气工程学院沈阳 110870
2.
沈阳工业大学信息科学与工程学院沈阳 110870
3.
东北大学信息科学与工程学院沈阳 110819

基金项目:

辽宁省教育厅项目 LQGD2017035

辽宁省自然科学基金 2015020064

国家自然科学基金 61102124

详细信息

作者简介:
郭佳强沈阳工业大学信息科学与工程学院硕士研究生.主要研究方向为智能控制, 复杂系统建模.E-mail:guo_dataworld@163.com

钱小毅沈阳工业大学电气工程学院博士研究生.主要研究方向为智能优化, 复杂机电装备的故障诊断.E-mail:qianxiaoyi123@163.com

王建辉博士, 东北大学信息科学与工程学院教授.主要研究方向为智能控制, 复杂系统建模, 康复机器人.E-mail:wangjianhui@ise.neu.ediu.cn

通讯作者:
张宇献沈阳工业大学电气工程学院副教授.2007年获得东北大学控制理论与控制工程专业博士学位.主要研究方向为智能控制, 复杂系统建模, 智能优化.本文通信作者.E-mail:yuxian524524@163.com

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出版历程
- 收稿日期: 2017-12-11
- 录用日期: 2018-02-26
- 刊出日期: 2019-09-20

An Adaptive Network-based Fuzzy Inference System with Mixed Data Inputs

1.
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870
2.
School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870
3.
College of Information Science and Engineering, Northeastern University, Shenyang 110819

Funds:

Educational Commission of Liaoning Province LQGD2017035

Natural Science Foundation of Liaoning Province 2015020064

National Natural Science Foundation of China 61102124

More Information

Author Bio:
Master student at the School of Information Science and Engineering, Shenyang University of Technology. His research interest covers intelligent control and complex system modeling

Ph.D. candidate at the School of Electrical Engineering, Shenyang University of Technology. His research interest covers intelligent optimization and fault diagnosis for complex mechanical and electrical equipment

Ph.D. professor at the College of Information Science and Engineering, Northeastern University. Her research interest covers intelligent control, complex system modeling and rehabilitation robot

Corresponding author: ZHANG Yu-Xian Associate professor at the School of Electrical Engineering, Shenyang University of Technology. He received his Ph. D. degree from Northeastern University in 2007. His research interest covers intelligent control, complex system modeling and intelligent optimization. Corresponding author of this paper

摘要

摘要: 现有数据建模方法大多依赖于定量的数值信息，而对于数值与分类混合输入的数据建模问题往往根据分类变量组合建立多个子模型，当有多个分类变量输入时易出现子模型数据分布不均匀、训练耗时长等问题.针对上述问题，提出一种具有混合数据输入的自适应模糊神经推理系统模型，在自适应模糊推理系统的基础上，引入激励强度转移矩阵和结论影响矩阵，采用基于高氏距离的减法聚类辨识模型结构，通过混合学习算法训练模型参数，使数值与分类混合数据对模糊规则的前后件参数同时产生作用，共同影响模型输出.仿真实验分析了分类数据对模型规则后件的作用以及结构辨识算法对模糊规则数的影响，与其他几种混合数据建模方法对比表明本文所提出的模型具有较高的预测精度和计算效率.
- 自适应模糊推理系统 /
- 结构辨识 /
- 激励强度转移矩阵 /
- 后件影响矩阵 /
- 混合属性数据
Abstract: The available data modeling methods mostly depend on quantitative numerical information. But the data modeling with both numerical and categorical data input often has to build multiple sub-models on the basic of combination of categorical variables. It is likely to present unevenly data distribution of sub-models, time-consuming training process and other problems when the multiple categorical variables are input. For the above problems, an adaptive network-based fuzzy inference system with mixed data inputs is proposed. Based on the structure of the adaptive network-based fuzzy inference system, a firing-strength transform matrix and a consequent influence matrix are introduced. The subtractive clustering based on the Gaussian distance is adapted to identify structure of model, and a hybrid learning algorithm is used to train parameters of model. The numerical and categorical data play an important role on the antecedent and consequent parameters of fuzzy rules, and jointly affect the output of model. The simulation experiment analyzes the effect on categorical data to the consequent rules and structure identification to number of fuzzy rules. Comparing with others data modeling with mixed data inputs, the proposed model in this paper has higher prediction accuracy and computational efficiency.
- Adaptive network-based fuzzy inference system /
- structure identification /
- firing-strength transform matrix /
- consequent influence matrix /
- mixed attribute data
注释:

1) 本文责任编委刘艳军

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图 1 MDI-ANFIS结构

Fig. 1 Structure of MDI-ANFIS

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图 2 样本平均规则后件输出

Fig. 2 Average consequent output of samples

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图 3 模型训练误差对比

Fig. 3 Comparison of model training error

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图 4 模型预测结果对比

Fig. 4 Comparison of model prediction

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图 5 聚类结果对比图

Fig. 5 Comparison of clustering results

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图 6 模型训练误差

Fig. 6 Model training error

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图 7 MDI-ANFIS模型预测对比

Fig. 7 Prediction results comparison of MDI-ANFIS

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表 1 MDI-ANFIS混合学习算法

Table 1 Hybrid learning algorithm of MDI-ANFIS

参数集	算法
P^c, Pⁱ	LSE
P^t	LSE
P^p	BP

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表 2 两种算法的平均规则后件影响和误差

Table 2 Average consequent influences and errors of two algorithms

组号	样本点个数	平均规则后件值		预测误差
组号	样本点个数	C-ANFIS	MDI-ANFIS	C-ANFIS	MDI-ANFIS
1	200	19.659	17.735	2.040	1.519
2	200	18.905	36.270	1.690	1.463
3	200	21.323	27.297	2.980	1.881
4	400	34.202	66.760	3.230	1.604
5	400	14.050	39.905	2.330	2.145
6	400	16.385	35.070	2.510	2.002
7	500	18.901	17.804	3.680	2.194
8	600	21.659	30.857	2.290	2.395
9	600	16.299	22.267	2.800	2.242
10	600	18.426	34.818	3.730	2.187
平均值	410	19.981	32.878	2.728	1.963

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表 3 结构辨识性能对比

Table 3 Performance comparison of structure identification

组号	样本点个数	规则数		预测误差
组号	样本点个数	SC	GDSC	SC	GDSC
1	100	50	13	0.452	0.356
2	100	32	14	0.575	0.466
3	200	37	21	0.517	0.709
4	200	25	14	0.908	0.613
5	300	40	18	0.586	0.690
6	300	34	16	0.661	0.705
7	400	31	16	0.642	0.459
8	400	32	14	0.630	0.747
9	500	30	13	0.788	0.836
10	506	30	14	0.726	0.827
平均值	300	34	15	0.648	0.641

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表 4 UCI数据集模型误差对比

Table 4 Model error comparison on UCI dataset

数据集	样本个数	混合属性 (N, C)	预测误差						误差降低率
数据集	样本个数	混合属性 (N, C)	ANFIS	N-ANFIS	F-ANFIS	S-MLP	C-ANFIS	MDI-ANFIS	ANFIS	N-ANFIS	F-ANFIS	S-MLP	C-ANFIS
Abalone	4 177	7, 1	2.608	1.842	1.997	3.985	2.632	1.951	0.336	-0.056	0.023	1.04	0.349
Boston Housing	506	11, 2	0.779	0.631	0.657	7.096	0.824	0.638	0.221	-0.011	0.029	10.1	0.291
Auto MPG	398	4, 3	2.072	0.912	0.871	6.969	0.963	0.605	2.42	0.507	0.439	10.5	0.591
Servo	167	2, 2	1.012	0.060	0.051	3.119	0.362	0.025	39.4	1.40	1.04	123	13.4
TAE	151	1, 4	2.972	0.196	0.385	0.849	0.192	0.225	12.2	-0.128	0.711	2.77	-0.146
Zoo	101	1, 15	1.276	0.062	0.059	2.542	0.126	0.072	16.7	-0.138	-0.181	34.3	0.750
Heart Disease	303	6, 7	0.255	0.073	0.062	1.483	0.108	0.086	1.96	-0.151	-0.279	16.2	0.255
平均值	-	-	1.568	0.539	0.583	3.720	0.744	0.515	10.462	0.203	0.255	28.273	2.213

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