Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and syste...Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and system rules are defined as specific structures.Second,to speed up the convergence of the learning algorithm and lighten the oscillation,an improved descent method for FIS generation is developed.Furthermore, the convergence and the oscillation of the algorithm are system- atically analyzed.Third,using the information obtained from the previous phase,it can be decided in which region of the in- put space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased.Consequently,this produces a new and more appropriate structure.Finally,the proposed method is applied to the problem of nonlinear function approximation.展开更多
Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rule...Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.展开更多
In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
该文基于安全域理论提出一套较完整的区域综合能源系统(regional integrated energy system,RIES)安全预警流程。首先,建立RIES安全域模型并给出RIES安全边界计算方法,包括交流安全边界与直流安全边界。其次,介绍模糊推理与模糊综合评...该文基于安全域理论提出一套较完整的区域综合能源系统(regional integrated energy system,RIES)安全预警流程。首先,建立RIES安全域模型并给出RIES安全边界计算方法,包括交流安全边界与直流安全边界。其次,介绍模糊推理与模糊综合评判方法,用于预警中评估越限的严重程度。再次,提出RIES预警方法,能综合考虑系统N-0与N-1安全性从而发出预警或告警信号。该方法包括预警指标选取与评判、安全预警分级、预警原因分析、安全趋势预测等步骤。最后,用算例验证该方法的有效性。可知,该文提出的安全预警方法对于提升RIES的安全管控能力,具有一定应用价值。展开更多
迈尔斯-布里格斯人格类型指标分类(Myers-Briggs type indicator,MBTI)测验被认为是预测人格类型最热门和最可靠的方法之一,但传统的问卷调查或专业人士咨询的检测方式在实施过程中面临着高昂的人力和时间成本以及潜在的隐私泄露风险。...迈尔斯-布里格斯人格类型指标分类(Myers-Briggs type indicator,MBTI)测验被认为是预测人格类型最热门和最可靠的方法之一,但传统的问卷调查或专业人士咨询的检测方式在实施过程中面临着高昂的人力和时间成本以及潜在的隐私泄露风险。针对这类问题,本文提出一种基于自适应神经模糊推理系统(adaptive-network-based fuzzy inference system,ANFIS)的MBTI模型(ANFIS-MBTI)。该模型将深度神经网络与模糊逻辑推理有机融合,使其能够通过自学习和参数优化策略,灵活适应并精准捕捉社交文本数据中隐含的非线性、模糊和不确定性特征,自动识别出分析社交媒体数据集中的用户行为模式,从而揭示其在信息获取、决策制定及行为方式等方面的心理特质和性格特点。实验结果表明,本文构建的ANFIS-MBTI模型能够高效而准确地从社交文本中挖掘出16种不同的MBTI人格类型,其多层级特征融合机制使人格分类任务的自动化程度显著提升;同时通过模糊规则约束有效控制人工干预需求与数据隐私风险,为大规模在线人格分析提供了具有可扩展性的创新技术路径。展开更多
To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two diff...To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.展开更多
基金Supported by National Basic Research Program of China(973 Program)(2007CB714006)
文摘Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and system rules are defined as specific structures.Second,to speed up the convergence of the learning algorithm and lighten the oscillation,an improved descent method for FIS generation is developed.Furthermore, the convergence and the oscillation of the algorithm are system- atically analyzed.Third,using the information obtained from the previous phase,it can be decided in which region of the in- put space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased.Consequently,this produces a new and more appropriate structure.Finally,the proposed method is applied to the problem of nonlinear function approximation.
文摘Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
文摘该文基于安全域理论提出一套较完整的区域综合能源系统(regional integrated energy system,RIES)安全预警流程。首先,建立RIES安全域模型并给出RIES安全边界计算方法,包括交流安全边界与直流安全边界。其次,介绍模糊推理与模糊综合评判方法,用于预警中评估越限的严重程度。再次,提出RIES预警方法,能综合考虑系统N-0与N-1安全性从而发出预警或告警信号。该方法包括预警指标选取与评判、安全预警分级、预警原因分析、安全趋势预测等步骤。最后,用算例验证该方法的有效性。可知,该文提出的安全预警方法对于提升RIES的安全管控能力,具有一定应用价值。
基金supported by the National Natural Science Foundation of China (60902055)
文摘To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.
