Model adaptation in possibilistic instance-based reasoning

This paper extends the possibilistic approach to instance-based reasoning that has recently been developed in a companion paper. Within the framework of this approach, the similarity-guided extrapolation principle underlying instance-based learning is formalized by means of so-called possibility rules, a special type of fuzzy rules. Proceeding from this idea, a methodology has been outlined, which allows a human expert to specify a model of the inference mechanism in a linguistic way. In this paper, a method for adapting a linguistic model automatically to observed data is proposed. This extension frees the expert from specifying mathematical concepts such as similarity measures and membership functions of fuzzy sets precisely. Rather, the expert determines only the qualitative structure of the model, which is then "calibrated" bit using the cases stored in memory     

Two-layered Blogger identification model integrating profile and instance-based methods

This paper introduces a two-layered framework that improves the result of authorship identification within larger sample numbers of bloggers as compared with earlier work. Previous studies are mainly divided into two categories: profile-based and instance-based methods. Each of these approaches has its advantages and limitations. The two-layered framework presented here integrates the two previous approaches and presents a new solution to a key problem in authorship identification, namely the drop in accuracy experienced as the number of authors increases. The paper begins by illustrating the regular instance-based core model and the investigated features. It then introduces a new psycholinguistic profile representation of authors, presents similarity grouping extraction over profiles, and applies blogger identification utilizing the two-layered approach. The results confirm the improvement introduced by the proposed two-layered approach against our regular classifier, as well as a selected baseline, for an extended number of users.     

Fuzzy reasoning Petri nets

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.     

Fuzzy soft set-based approach to prioritizing technical attributes in quality function deployment

Quality function deployment is a planning and problem-solving tool for translating customer requirements into the technical attributes of a product. To obtain the rating order of technical attributes is a crucial step in applying quality function deployment. Based on the fuzzy soft theory proposed by Maji et al., which is a new mathematical tool to deal with uncertainties, a novel approach is proposed in this paper in order to prioritize technical attributes. We utilize the relative deviation between scores of customer requirements and their average to construct membership function in order to construct fuzzy soft sets. The weights of customer requirements and the final importance of technical attributes are obtained from fuzzy soft sets derived from raw data without other information. An illustrated example is cited to demonstrate the application of the proposed approach.     

Lyapunov based reasoning methods

Semiquantitative simulation is an approach for the analysis of uncertain dynamic systems that performs a comprehensive simulation study based on automated reasoning methods. Semiquantitative simulation of complex models is, however, hindered by the limited automated reasoning capabilities of the currently available semiquantitative simulation techniques. The paper describes the extension of semiquantitative simulation techniques on the basis of Lyapunov methods. This extension improves automated reasoning by utilizing generalized energy functions, called Lyapunov functions. Automated reasoning based on Lyapunov functions can be seen as a generalization of the energy considerations employed by engineers. It has the advantage that it can be used to analyze systems where it does not make sense to speak about energy in the physical sense. The difficult task of deducing a Lyapunov function for the semiquantitatively modeled dynamic system is solved by reformulating methods from nonlinear control theory. A procedure for an automatic deduction of a Lyapunov function and Lyapunov-based reasoning methods using this deduced Lyapunov function are given. The improved automated reasoning capabilities of our extended SQSIM simulation platform are demonstrated by example     

Fuzzy model of inexact reasoning in medicine

In this paper a fuzzy model of inexact reasoning in medicine is developed. Both diagnosis and choice of therapy are considered. In the context of diagnosis a model used as the mathematical basis of MYCIN is given in a fuzzy interpretation. Single combining functions are identified with basic fuzzy operations and principles. In choosing therapy, the approach adopted in MYCIN formalized in fuzzy set terminology. A modified model of therapy selection is developed based upon the certainty factors of possible significant organisms' identities.     

Fuzzy temporal reasoning for process supervision

Abstract: Process supervision consists of following the temporal evolution (change) of process behaviours. This task has usually been performed based on the knowledge and experience of domain experts and operators. Actually, these experts and operators almost always express their experience and knowledge about process evolution in an imprecise, fuzzy and vague way. A good supervision system should be capable of dealing at once with two different kinds of knowledge: time and uncertainty.
For many years, time and uncertainty have been two of the most important topics in Artificial Intelligence research and applications. Many approaches have been proposed to deal with either one or the other. Among the various approaches for time, reified logic has been considered as the most influent one. Possibilistic logic, on the other hand, has shown its ability to handle uncertain knowledge and information. This paper describes an approach for managing temporal uncertainty based on fuzzy logic and possibility theory. A fuzzy temporal expert system shell has been developed to perform process supervision tasks.     

Fuzzy backward reasoning using fuzzy Petri nets

Chen, Ke and Chang (1990) have presented a fuzzy forward reasoning algorithm for rule-based systems using fuzzy Petri nets. In this paper, we extend the work of Chen, Ke and Chang (1990) to present a fuzzy backward reasoning algorithm for rule-based systems using fuzzy Petri nets, where the fuzzy production rules of a rule-based system are represented by fuzzy Petri nets. The system can perform fuzzy backward reasoning automatically to evaluate the degree of truth of any proposition specified by the user. The fuzzy backward reasoning capability allows the computers to perform reasoning in a more flexible manner and to think more like people.     

支持模糊粗糙本体的推理机研究

不精确信息进行表达和推理逐渐成为一种必然的需求,而经典的本体并不适合处理这种不确定的信息.设计了一种支持模糊粗糙本体的推理机,其最大的特点是处理的对象是模糊粗糙本体,从而解决了不确定信息的推理问题.对模糊粗糙本体模型进行了研究,着重介绍了该模糊粗糙本体推理机的总体结构、功能及推理机中各模块的作用,通过对模糊粗糙本体进行约简和去粗糙处理,最终将对模糊粗糙本体的推理转化为经典的推理.     

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.     

基于模糊推理的模糊原型网络

针对真实数据具有的模糊性和不确定性会严重影响小样本学习分类结果这一问题,改进并优化了传统的小样本学习原型网络,提出了基于模糊推理的模糊原型网络(FPN).首先,从卷积神经网络(CNN)和模糊神经网络两个方向分别获取图像特征信息;然后,对获得的两部分信息进行线性知识融合,得到最终的图像特征;最后,度量各个类别原型到查询集...     

Fuzzy reasoning approach to similarity evaluation in image analysis

In image analysis, the concept of similarity has been widely explored and various measures of similarity, or of distance, have been proposed that yield a quantitative evaluation. There are cases, however, in which the evaluation of similarity should reproduce the judgment of a human observer based mainly on qualitative and, possibly, subjective appraisal of perceptual features. This process is best modeled as a cognitive process based on knowledge structures and inference strategies, able to incorporate the human reasoning mechanisms and to handle their inherent uncertainties. This articlea proposes a general strategy for similarity evaluation in image analysis considered as a cognitive process. A salient aspect is the use of fuzzy logic propositions to represent knowledge structures, and fuzzy reasoning to model inference mechanisms. Specific similarity evaluation procedures are presented that demonstrate how the same general strategy can be applied to different image analysis problems. © 1993 John Wily & Sons, Inc.     

空间关系推理的研究方法

空间关系推理在地理信息系统、空间数据挖掘、模式识别、机器人学、专家系统、图形与图像处理和自然语言理解等方面具有广泛的应用。近年来,国内外专家对空间关系推理进行了大量的研究,取得了一系列的研究成果。介绍了国内外在空间关系推理方面的研究方法和研究成果,并且进行了比较和分析,概括了已有方法的特点和不足,讨论了该领域未来的研究重点和难点。     

Optimized learning instance-based image retrieval

Multimedia Tools and Applications - Image retrieval is a recognition technique in the field of computer vision. In most cases, high-quality retrieval is often supported by adequate learning...     

Fuzzy reasoning based on the extension principle*

According to the operation of decomposition (also known as representation theorem) (Negoita CV, Ralescu, DA. Kybernetes 1975;4:169–174) in fuzzy set theory, the whole fuzziness of an object can be characterized by a sequence of local crisp properties of that object. Hence, any fuzzy reasoning could also be implemented by using a similar idea, i.e., a sequence of precise reasoning. More precisely, we could translate a fuzzy relation “If A then B” of the Generalized Modus Ponens Rule (the most common and widely used interpretation of a fuzzy rule, A, B, are fuzzy sets in a universe of discourse X, and of discourse Y, respectively) into a corresponding precise relation between a subset of P(X) and a subset of P(Y), and then extend this corresponding precise relation to two kinds of transformations between all L-type fuzzy subsets of X and those of Y by using Zadeh's extension principle, where L denotes a complete lattice. In this way, we provide an alternative approach to the existing compositional rule of inference, which performs fuzzy reasoning based on the extension principle. The approach does not depend on the choice of fuzzy implication operator nor on the choice of a t-norm. The detailed reasoning methods, applied in particular to the Generalized Modus Ponens and the Generalized Modus Tollens, are established and their properties are further investigated in this paper. © 2001 John Wiley & Sons, Inc.