Fuzzy reasoning model under quotient space structure

In this paper, we present a theoretical framework of fuzzy reasoning model under quotient space structure. It consists of (1) introducing quotient space structure into fuzzy sets, i.e., constructing fuzzy set representations of different grain-size spaces and their relationships; (2) introducing the concept of fuzzy sets into quotient space theory, i.e., introducing fuzzy equivalence relation and discussing its corresponding reasoning in different grain-size spaces; and (3) discussing the relationship and transformation among different granular computing methodologies. The framework proposed is aimed to combine two powerful abilities in order to enhance the efficiency of fuzzy reasoning: one is the ability of computing with words based on fuzzy set methodology, the other is the ability of hierarchical problem solving based on quotient space approach.     

基于属性粒度商空间的本体形式化与检验

针对传统的本体形式化过程中从一个本体层次空间跳转到另一个本体层次空间存在的问题,将粒度计算思想引入本体建模领域,利用属性粒度商空间理论构建本体形式化模型,定义模型的各个部分,在此基础上对基于属性粒度商空间的本体形式化模型进行检验,验证该模型可以较好地满足本体层次之间的跳转及推理关系。     

基于动态模糊粒神经网络算法的负荷辨识

针对电力负荷的时变、变结构和非线性等特点,提出一种动态模糊粒神经网络算法.该算法采用粒计算商空间理论和模糊神经网络技术对电力负荷进行建模.将椭圆基函数和模糊ζ-完备性作为在线参数分配机制,避免了初始化选择的随机性.根据模糊规则和输入变量的重要性,对每条规则的输入变量宽度实施在线自适应调整,从而实现了负荷参数和结构同时辨识.实验结果表明了所提出方法的可行性和有效性.     

Truth-value transmittal fuzzy reasoning interpolator

Fuzzy reasoning and interpolation were used independently as two methods of ex- pression functional relationship approximately in the past. The relevancy behind them was discussed firstly in refs. [1―3]. In ref. [1], fuzzy reasoning algorithms used com- monly at present were approximated to some interpolation algorithms. Based on the analysis of rationality of such approximation, the author of ref. [4] put forward the con- cept of the fuzzy reasoning interpolator. In this paper, we keep on re…     

一种蕴涵算子下FMP模型的新型反向三I算法

针对Fuzzy推理中提出的“过半可信”原则,证明了[RG]蕴涵算子满足这一原则,并讨论了[RG]蕴涵算子下FMP模型的新型反向三I支持算法,新型[α-]反向三I支持算法,新型[α-]反向三I约束算法的计算公式,且给予证明。     

基于模糊粗糙集的粒度计算

粒度计算是一种新的智能计算的理论和方法，目前受到很多学者的关注。但是，具体可行的粒表示模型和不同粒的推理方法研究相对较少。本文将模糊粗糙集纳入粒度计算这种新的理论框架，对于处理复杂信息系统，求解复杂问题无疑具有重要的意义。首先利用笛卡尔积，构建了模糊关系下的信息粒；然后给出不同粒度下模糊粗糙算子的表示方法，进而形成一个分层递阶结构；最后考虑了对于模糊信息系统粒度粗细的选择问题，并给出一个实例，从而为粒度计算提供一个具体而实用的框架。     

A smart conflict resolution model using multi-layer knowledge graph for conceptual design

Reducing the impact of conflicts on requirement-function-structure mapping in the early stage of product design is an important measure to achieve conceptual innovation, which relies on accurate reasoning of multi-domain knowledge. As product requirements become more personalized and diverse, traditional discrete knowledge organization and reasoning methods are difficult to adapt to the challenges of continuity and precision in conceptual solution. Knowledge graphs with complex networks have obvious advantages in association detection, knowledge visualization, and explainable reasoning of implicit knowledge, which offer innovative opportunities for conflict resolution in conceptual design. Therefore, a smart conflict resolution model using a multi-layer Knowledge Graph for Conceptual Design(mKGCD) is proposed in this study. A knowledge expression form of FBS-oriented design patent vocabulary is proposed, which is used for knowledge entity recognition and relation extraction based on natural language processing. A label mapping method based on inventive principles is used for patent classification and a four-layer semantic network for conflict resolution is constructed. Through semantic distance calculation, the designer's requirements for function/behavior/structure are smart deployed to obtain appropriate knowledge. A case study of the conceptual design of a collapsible installation and handling equipment demonstrates the feasibility of the proposed approach. The proposed method can not only meet the functional solution and innovation in the context of different design requirements, but also effectively improve the design efficiency in the iterative design process by means of multiple meanings of one graph.     

模糊推理三I算法的连续性和逼近性

三I算法是一种新的模糊推理方法,可以作为传统的模糊推理方法的修改和补充.系统地研究了三I支持度算法和反向三I支持度算法的连续性问题,并指出了基于一些常用的蕴涵算子的三I算法具有逼近性.此结果对构建模糊控制系统和模糊专家系统时选用三I推理算法具有一定的指导作用.     

二进制粒计算模型

粒计算是一种处理不确定性数据的理论方法,涵盖粗糙集、模糊集、商空间、词计算等。目前,数据的粒化与粒的计算主要涉及集合的运算与度量,集合运算的低效制约着粒计算相关算法的应用领域。为此,提出了一种二进制粒计算模型,给出了粒的三层结构,包括粒子、粒群与粒库,并定义了二进制粒子及二进制粒子的运算,将传统的集合运算转化为二进制数的计算,进一步给出了二进制粒子的距离度量,将等价类的集合表示方式转化为粒子的距离度量表示方式,给出了粒子距离的相关性质。该模型定义了二进制粒群距离的概念,给出了二进制粒群距离的计算方法,提出了基于二进制粒群距离的属性约简方法,证明了该方法与经典粗糙集约简方法的等价性,并以二进制粒群距离作为启发式信息,给出了两种约简算法。     

基于极大模糊熵原理的模糊推理反向三I算法

提出了用模糊熵来度量反向三I模糊推理结果的模糊程度，给出了模糊熵反向三I原则，讨论了FMP和FMT问题的模糊熵反向三I支持算法解存在的条件，分别给出了几个常见蕴涵算子的FMP问题与FMT问题的模糊熵反向三I解的计算公式．     

基于自组织神经网络的非线性系统建模

针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度.     

基于三Ⅰ算法的模糊系统的响应能力

探讨模糊系统的函数逼近能力是模糊系统理论研究的一个重要的课题.本文首次讨论了在两种推理规则情形下由三Ⅰ支持度算法和模糊熵三Ⅰ算法设计的模糊系统的响应能力.针对三Ⅰ支持度算法,分别就正则蕴涵算子和11个具体的模糊蕴涵算子,考察了相应模糊系统的响应能力,讨论了基于模糊熵三Ⅰ算法和三Ⅰ算法设计的模糊系统的响应函数之间的关系....     

The modeling of time series based on fuzzy information granules

A lot of research has resulted in many time series models with high precision forecasting realized at the numerical level. However, in the real world, higher numerical precision may not be necessary for the perception, reasoning and decision-making of human. Model of time series with an ability of humans to perceive and process abstract entities (rather than numeric entities) is more adaptable for some problems of decision-making. With this regard, information granules and granular computing play a primordial role. Fox example, if change range (intervals) of stock prices for a certain period in the future is regarded as information granule, constructing model that can forecast change ranges (intervals) of stock prices for a period in the future is better able to help stock investors make reasonable decisions in comparison with those based upon specific forecasting numerical value of stock price. In this paper, we propose a new modeling approach to realize interval prediction, in which the idea of information granules and granular computing is integrated with the classical Chen’s method. The proposed method is to segment an original numeric time series into a collection of time windows first, and then build fuzzy granules expressed as a certain fuzzy set over each time windows by exploiting the principle of justifiable granularity. Finally, fuzzy granular model can be constructed by mining fuzzy logical relationships of adjacent granules. The constructed model can carry out interval prediction by degranulation operation. Two benchmark time series are used to validate the feasibility and effectiveness of the proposed approach. The obtained results demonstrate the effectiveness of the approach. Besides, for modeling and prediction of large-scale time series, the proposed approach exhibit a clear advantage of reducing computation overhead of modeling and simplifying forecasting.     

A systematic decision making approach for product conceptual design based on fuzzy morphological matrix

Conceptual design plays an important role in development of new products and redesign of existing products. Morphological matrix is a popular tool for conceptual design. Although the morphological-matrix based conceptual design approaches are effective for generation of conceptual schemes, quantitative evaluation to each of the function solution principle is seldom considered, thus leading to the difficulty to identify the optimal conceptual design by combining these function solution principles. In addition, the uncertainties due to the subjective evaluations from engineers and customers in early design stage are not considered in these morphological-matrix based conceptual design approaches. To solve these problems, a systematic decision making approach is developed in this research for product conceptual design based on fuzzy morphological matrix to quantitatively evaluate function solution principles using knowledge and preferences of engineers and customers with subjective uncertainties. In this research, the morphological matrix is quantified by associating the properties of function solution principles with the information of customer preferences and product failures. Customer preferences for different function solution principles are obtained from multiple customers using fuzzy pairwise comparison (FPC). The fuzzy customer preference degree of each solution principle is then calculated by fuzzy logarithmic least square method (FLLSM). In addition, the product failure data are used to improve product reliability through fuzzy failure mode effects analysis (FMEA). Unlike the traditional FMEA, the causality relationships among failure modes of solution principles are analyzed to use failure information more effectively through constructing a directed failure causality relationship diagram (DFCRD). A fuzzy multi-objective optimization model is also developed to solve the conceptual design problem. The effectiveness of this new approach is demonstrated using a real-world application for conceptual design of a horizontal directional drilling machine (HDDM).     

FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices

Defining a relation between granules and computing ever-changing granules are two important issues in granular computing. In view of this, this work proposes a partial order relation and lattice computing, respectively, for dealing with the aforementioned issues. A fuzzy lattice granular computing classification algorithm, or FL-GrCCA for short, is proposed here in the framework of fuzzy lattices. Algorithm FL-GrCCA computes a fuzzy inclusion relation between granules by using an inclusion measure function based on both a nonlinear positive valuation function, namely arctan, and an isomorphic mapping between lattices. Changeable classification granules are computed with a dilation operator using, conditionally, both the fuzzy inclusion relation between two granules and the size of a dilated granule. We compare the performance of FL-GrCCA with the performance of popular classification algorithms, including support vector machines (SVMs) and the fuzzy lattice reasoning (FLR) classifier, for a number of two-class problems and multi-class problems. Our computational experiments showed that FL-GrCCA can both speed up training and achieve comparable generalization performance.     

Design candidate identification using neural network-based fuzzy reasoning

Conceptual design has profound impact on success of a product design. Identification of the best conceptual design candidate is a crucial step as design information is not complete and design knowledge is minimal at conceptual design stage. This paper presents a method for design candidate evaluation and identification using neural network-based fuzzy reasoning. The method consists of the following steps: (1) acquisition of customer needs and ranking of their importance, (2) establishment of measurable metrics and their relations with customer needs, (3) development of design specifications and initial evaluation of design candidates, and (4) evaluation and identification of design candidates based on design specifications and customer needs using neural network-based fuzzy reasoning. A case study is given to show the effectiveness of the proposed method and associated algorithms.     

基于规则推理的FPN误用入侵检测方法

针对网络入侵攻击活动的模糊性，提出了一种基于模糊推理的模糊Petri网（FPN）误用入侵检测方法。该方法定义了一个六元组FPN，并将模糊产生式规则精化为两种基本类型。在此基础上给出了FPN表示模糊规则的模型、推理过程和基于FPN的推理算法。最后通过入侵检测的实例对该方法的正确性和有效性进行了验证，结果表明该方法推理过程简单直观、容易实现，而且具有并行推理能力，可适用于大规模的FPN模型，是误用入侵检测技术的一种非常有效的解决方案。     

一种基于改进T-S模糊推理的模糊神经网络学习算法

针对模糊神经网络学习算法计算量过大,在预测模型设计中提出了基于改进T-S模糊推理的模糊神经网络学习算法。主要工作如下:首先,改进T-S模糊推理方法,定义基于偏移率的T-s模糊推理方法;然后,通过将此模糊推理方法与基于合成规则的模糊推理方法及距离型模糊推理方法相比较可以看出,所提方法有较少的计算量,且比较有效;最后,在此基础上改善了模糊神经网络学习算法,并将其应用于天气预测与安全态势预测。测试结果表明,该方法明显改善了学习效率,减少了预测模型设计中的学习次数与时间复杂度,并降低了学习误差。     

Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory

This paper presents a fuzzy modeling method proposed by Wang and Mendel for generation of fuzzy rules using data generated from a simulated model that is built from a real factory located in Hsin-Chu science-based park of Taiwan, R.O.C. The fuzzy modeling method is further evolved by a genetic algorithm for due-date assignment problem in manufacturing. By using simulated data, the effectiveness of the proposed method is shown and compared with two other soft computing techniques: multi-layer perceptron neural networks and case-based reasoning. The comparative results indicate that the proposed method is consistently superior to the other two methods.     

基于蕴涵算子L_p的模糊推理的FMP反向三I算法

研究了基于蕴涵算子Lp模糊推理的FMP反向三I支持算法及α-反向三I支持算法,给出了FMP模型的反向三I算法及α-反向三I算法的计算公式。