SVD-based complexity reduction of rule-bases with nonlinearantecedent fuzzy sets

With the help of the singular value decomposition (SVD) based complexity reduction method, not only can the redundancy of fuzzy rule-bases be eliminated, but further reduction can also be made, considering the allowable error. Namely, in the case of higher allowable error, the result may be a less complex fuzzy inference system, with a smaller rule-base. This property of the SVD-based reduction method makes possible the usage of fuzzy systems, even in cases when the available time and resources are limited. The original SVD-based reduction method was proposed for rule-bases with linear antecedent fuzzy sets. This limitation remained valid in the later extensions, as well. The purpose of this paper is to give a formal mathematical proof for the original formulas with nonlinear antecedent fuzzy sets and thus to end this limitation     

A fuzzy model for evaluation and prediction of slurry erosion of 5127 steels

This paper introduces a fuzzy logic model to evaluate and predict the slurry erosion of ductile materials in engineering and chemical applications. There are seven variables which have the most significant influences on the slurry erosion of ductile material, and must be taken into considerations. These are impact angle, time, roundness factor, aspect ratio, particle size, impact velocity and concentration of the particles. By using the fuzzy rules which can be generated based on some experimental observations, a two-layer fuzzy model could be developed to correlate these variables to the slurry erosion (weight loss of material). The model is based on the assumption that the slurry erosion characteristics of ductile materials is an imprecise complex function of many interacting variables, and can be described and evaluated by the theory of fuzzy sets. Membership functions and fuzzy rule-base for application to slurry erosion of 5127 steel as a ductile material are presented in detail to demonstrate the effectiveness of fuzzy logic in modeling such a complex system in an efficient way. This constructed model would facilitate the development of an expert, real-time and integrated system using fuzzy logic technology to monitor the level of the slurry erosion process.     

A new approach to dynamic fuzzy modeling of genetic regulatory networks

In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks.     

Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

Intuitionistic fuzzy Petri net is an important class of Petri nets, which can be used to model the knowledge base system based on intuitionistic fuzzy production rules. In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems, a new Petri net modeling method is proposed by introducing BP (Error Back Propagation) algorithm in neural networks. By judging whether the transition is ignited by continuous function, the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training, which makes Petri network have stronger generalization ability and adaptive function, and the reasoning result is more accurate and credible, which is useful for information services. Finally, a typical example is given to verify the effectiveness and superiority of the parameter optimization method.     

基于模糊规则的非线性系统快速建模方法

描述了一个通过学习从输入输出采样数据中提取MISO模糊规则的具有插值性能的新型模糊系统。提出的模糊模型继承了Sugeno模型及其变化形式的许多优点。对于新的模糊模型采用递推最小二乘估计方法代替启发式误差反馈学习方法辨识结论参数，整个辨识过程所需的计算时间相对于其他方法最短，适用于在线辨识。仿真结果表明了该方法的有效性与实用性。     

A Robust Design Method in Product Quality Applications

1　IntroductionThe 1980bookletbyTaguchiwaswrittenatatimewhentheinitialsignal to noiseratioswerebeingdevel opedandhisthinkingwasbeingtranslatedfromJapaneseforthefirsttime.Histhinkingandourunder standingofhisthinkinghaveevolvedagreatdealduringtheensuing 2 1years.Taguchi′sapproachestooff linequalityimprovementhavegeneratedmuchinterestanddebateduringthelast2 1years.Foranaccountofthesetechniques ,seeTaguchiandWu( 1980 ) .Thishasinturnledresearcherstocloselyexaminehismeth odsandsuggestvariousimpr…     

Fuzzy Design Method of Product Quality Robustness

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基于区间二型模糊EDAS的多属性决策方法

针对多属性决策存在的复杂性和不确定性问题,提出基于区间二型模糊平均解距离法 (evaluation based on distance from average solution,EDAS) 的多属性决策方法。采用区间二型模糊集合 (interval type-2 fuzzy sets,IT2FS) 表达评价信息解决专家的偏好信息存在个体化差异问题,并纳入EDAS对备选方案进行排序。以区间二型模糊数表达评价信息构建决策矩阵,以计算得到的综合评价值的去模糊化结果作为最终的方案排序依据。针对EDAS中属性权重需要从外部获取的问题,采用区间二型模糊集合改进的最优最劣法 (best-worst method,BWM) 确定属性权重。最后,以某汽车制造企业选购新能源汽车云服务方案为例,验证所提方法的有效性。     

Fuzzy rule-based image processing

A novel image-processing technique based on fuzzy rules is proposed. This technique uses human knowledge about how an image should be processed depending on the local characteristics of image. The knowledge is represented by fuzzy rules and is also represented by a multidimensional nonlinear function which can be optimized by training. Efficient noise reduction and edge detection are realized by this fuzzy rule-based method. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 457–461, 1997     

A new method for solving single and multi-objective fuzzy minimum cost flow problems with different membership functions

Several authors have proposed different methods for solving fuzzy minimum cost flow (MCF) problems. In this paper, some single and multi-objective fuzzy MCF problems are chosen which cannot be solved by using any of the existing methods and a new method is proposed for solving such type of problems. The main advantage of the proposed method over existing methods is that the fuzzy MCF problems which can be solved by using the existing methods can also be solved by the proposed method. But, there exist several fuzzy MCF problems which can be solved only by using the proposed method i.e., it is not possible to solve these problems by using the existing methods. To illustrate the proposed method and also to show the advantages of the proposed method over existing methods some single and multi-objective fuzzy MCF problems which cannot be solved by using the existing methods are solved by using the proposed method and the obtained results are discussed.     

基于模糊Petri网考虑制造约束的零件

加工方案决策是计算机辅助工艺规划中一直未能完全解决的关键问题.首先,在给出Petri网系统和T_不变量定义的基础上,根据Petri网原理和产生式规则建立一个基于模糊规则的零件表面加工方案Petri网模型;然后,根据Petri网T_不变量在考虑制造资源约束的条件下,提出一种新的可选加工方案T_不变量生成方法;最后,通过实例验证了所提方法的有效性.实例结果表明,采用作者提出的决策方法能够获得零件表面加工的最优/次优工艺方案     

An efficient genetic algorithm for TSK‐type neural fuzzy identifier design

Abstract

In this paper, an efficient genetic algorithm (EGA) of the Takagi‐Sugeno‐Kang (TSK) ‐type neural fuzzy identifier (TNFI) is proposed for solving various identification problems. For the proposed EGA method, the better chromosomes will be initially generated while the better mutation points will be determined to perform efficient mutation. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are precise. Secondly, the proposed EGA method only requires a small population sizes.     

不同信息融合方法在结构损伤识别上的应用和分析

在工程结构的损伤探测领域,不同的信息融合方法和方式对结构的损伤敏感程度以及计算的复杂程度往往不同,而且适用条件也不同。为了解决以上问题,描述了基于结构损伤识别的功能信息融合模型,并在此基础之上采用了多种融合方法进行了数值仿真和分析。数值仿真结果表明,采用了信息融合技术的结构多损伤位置识别,可以产生比单一信息源更精确、更完全的估计和判决,而且不同的信息融合算法的应用往往取决于研究对象和实际条件的要求。     

模糊粗糙集在轴承故障模式识别中的应用

提出一种基于模糊粗糙集理论的模式识别方法,将动态聚类法和方差分析法引入连续属性模糊化,获取模糊隶属函数,避开了粗糙集理论属性离散化过程带来的信息丢失;利用F检验判断分类的合理性,克服了人为确定分类数目的缺点;应用模糊化得到的模糊决策表进行条件属性约简,通过属性值约简,提取了清晰、简明的故障模式规则。轴承故障模式识别结果表明,该方法对比一般粗糙集理论,有效地提高了模式识别精度,在实际模式识别中具有很好的应用价值。     

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly, the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm. The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL. After that, the reasoning process, including the SSFBN structure algorithm, data fuzzification, reasoning of fuzzy rules, and fuzzy belief propagation, is scheduled. Finally, compared with the classical algorithm from the aspect of accuracy and time complexity, our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity, which proves the feasibility and validity of our solution to represent and reason uncertain information.     

基于信息熵与谱有限元法的传感器优化布置

为从测量数据中获得尽可能多信息,减少待识别模型参数的不确定性,提出面向结构模型参数识别的传感器优化布置方法。为避免用静态形函数传统有限元方法建模对结构动力特性及传感器优化布置影响,采用高精确动力学法即谱有限元法对结构进行动力学建模。以结构模型参数识别结果的不确定性最小作为传感器优化布置准则,该不确定性程度通过信息熵标量指标量化,用贝叶斯统计系统识别法进行识别。采用整数编码遗传算法在所有可能的传感器配置组合中极小化信息熵指标,获得给定数目的传感器最优布置位置。通过弹性地基带弹性接头的周期管梁模型数值仿真及模型试验验证所提方法。     

基于模糊神经网络的数据融合结构损伤识别方法

为了有效利用结构健康监测系统中的多源传感器数据信息,提高损伤检测与评估的识别正确率,该文通过构造模糊神经网络分类器,提出了一种基于模糊神经网络的数据融合损伤识别方法并将之应用于结构健康诊断中。它先通过数据预处理,提取原始响应信号中的特征参数,接着将之作为模糊神经网络的输入,构造模糊神经网络模型进行识别决策,最后运用数据融合算法,计算出数据融合后的决策结果。为了验证所提方法的有效性,通过一个7自由度的建筑模型,分别用单一模糊神经网络决策器和数据融合损伤识别方法进行了损伤识别和比较。研究结果表明:该文所提方法比单一决策结果更准确、可靠。     

An Efficient Genetic Hybrid PAPR Technique for 5G Waveforms

Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol. In this study, an efficient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm (BFOA) for PTS (BFOA-PTS). The PAPR minimization is accomplished in a two-stage process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.     

同向回转双机传动振动系统相位差的模糊监督控制

通过机械系统动力学分析和计算机仿真结果相结合,提出了同向回转双机传动振动系统中两个偏心转子的相位差的模糊监督控制方法。利用机械动力学分析和计算机模拟相结合获得模糊控制规则的方法,为具有复杂数学模型的非线性系统的模糊控制提供了一条新途径,丰富了控制规则的来源。     

A prototype fuzzy resistance spot welding system

In assembly automation, the need to perform on-line quality monitoring and to provide near real-lime inspection of resistance spot welds, has attained unsurpassed importance in industries. This paper describes the development of a prototype fuzzy control system for a two-stage resistance spot welding machine with potential of being incorporated into an automation line. It encompasses a new approach aimed at providing simultaneous control of more than two major welding parameters for achieving consistent quality spot welds by assessing the condition of the heat affected zone created. The prototype system, developed in C language, comes with a decision making mechanism and a quick search mechanism. Its rule-base contains a total of 125 heuristic control rules derived from literature reviews and interviews with experts and through experiments. The various factors affecting the quality of spot welds are outlined in the paper. The correlation between spot weld quality and welding parameters were examined using the Taguchi technique. Details of the prototype system are described and the results of the experiments conducted on the system are discussed.