A normal form which preserves tautologies and contradictions in a class of fuzzy logics

Most of the normal forms for fuzzy logics are versions of conjunctive and disjunctive classical normal forms. Unfortunately, they do not always preserve tautologies and contradictions which is important, for example, for automated theorem provers based on refutation methods.De Morgan implicative systems are triples like the De Morgan systems, which consider fuzzy implications instead of t-conorms. These systems can be used to evaluate the formulas of a propositional language based on the logical connectives of negation, conjunction and implication. Therefore, they determine different fuzzy logics, called implicative De Morgan fuzzy logics.In this paper, we will introduce a normal form for implicative De Morgan systems and we will show that for implicative De Morgan fuzzy logics whose t-norms are strict, this normal form preserves contradictions as well as tautologies.     

Γ-群的Fuzzy同余

本文介绍了Γ－群的Ｆｕｚｚｙ核正规系的概念,并通过该概念刻画了Γ－群的Ｆｕｚｚｙ同余,得出和Γ－群同余刻画相类似的结论。     

Fuzzy systems based on component software

This paper describes hierarchical modeling of fuzzy logic concepts that has been used within the recently developed model of intelligent systems, called OBOA. The model is based on a multilevel, hierarchical, general object-oriented approach. Current methods and software design and development tools for intelligent systems are usually difficult to extend, and it is not easy to reuse their components in developing intelligent systems. The OBOA model tries to reduce these deficiencies. The model starts with a well-founded software engineering principle, making clear distinction between generic, low-level intelligent software components, and domain-dependent, high-level components of an intelligent system. This paper concentrates on modeling and implementation of fuzzy logic concepts within the hierarchical levels of the OBOA model. The fuzzy components described are extensible and adjustable. As an illustration of how these components are used in practice, a practical design example from the domain of medical diagnosis is shown. The paper also suggests some steps towards future design of fuzzy components and tools for intelligent systems.     

Program control of operating systems

The traditional job control language becomes superfluous if the existing programming languages are extended slightly. Such extensions also allow drivers and operating systems to be programmed entirely in high level languages. Ultimately, we may see machine independent operating systems. A framework is presented for an extendable operating system which allows a simple, uniform implementation of these language extensions.     

一类用于复杂系统Fuzzy控制的因果关系归纳推理机制

本文以复杂系统的Ｆｕｚｚｙ控制为背景，在Ｆｕｚｚｙ语言场与Ｆｕｚｚｙ语言值结构的描述架下，构造了一类在大范围内适用的因果关系归纳推理机制，并找到了它在Ｆｕｚｚｙ控制中的应用。     

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of natural language that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network is designed to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i.e. specific determinants are used for each industry). The results show that the credit rating classes assigned to bond issuers can be classified with high classification accuracy using low numbers of features, membership functions, and if-then rules. The comparison of selected fuzzy rule-based classifiers indicates that it is possible to increase classification performance by using different classifiers for individual industries.     

Ten years of genetic fuzzy systems: current framework and new trends

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms.The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.     

Formalizing argumentative reasoning in a possibilistic logic programming setting with fuzzy unification

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at the object-language level. In spite of its expressive power, an important limitation in P-DeLP is that imprecise, fuzzy information cannot be expressed in the object language. One interesting alternative for solving this limitation is the use of PGL⁺, a possibilistic logic over Gödel logic extended with fuzzy constants. Fuzzy constants in PGL⁺ allow expressing disjunctive information about the unknown value of a variable, in the sense of a magnitude, modelled as a (unary) predicate. The aim of this article is twofold: firstly, we formalize DePGL⁺, a possibilistic defeasible logic programming language that extends P-DeLP through the use of PGL⁺ in order to incorporate fuzzy constants and a fuzzy unification mechanism for them. Secondly, we propose a way to handle conflicting arguments in the context of the extended framework.     

基于综合函数设计的模糊系统及其插值机理

分析现有模糊系统的数学表示形式,注意到模糊系统输出函数的构造常采用对规则后件模糊集峰点(中心)值加权平均的方法. 综合函数是因素空间理论中利用简单因素状态合成复杂因素状态的有效工具, 本文将综合函数用于构造规则后件模糊集峰点(中心)值的权重, 给出一种新的模糊系统设计方法, 并且证明了这种基于综合函数的模糊系统可归结为一种插值函数.     

求极大T-传递内部的新方法

利用Fodor等的基本思想,从另一个角度构造了一个有限论域上模糊关系的极大T-传递内部,求得另一个与Fodor等的方法不同的极大T-传递内部,并在计算机上加以实现,从而为将一个非传递的模糊关系改造为传递的模糊关系提供了一个新的方法.     

Extending possibilistic logic over Gödel logic

In this paper we present several fuzzy logics trying to capture different notions of necessity (in the sense of possibility theory) for Gödel logic formulas. Based on different characterizations of necessity measures on fuzzy sets, a group of logics with Kripke style semantics is built over a restricted language, namely, a two-level language composed of non-modal and modal formulas, the latter, moreover, not allowing for nested applications of the modal operator N. Completeness and some computational complexity results are shown.     

Fuzzy prioritized operators and their application to multiple attribute group decision making

In this paper, we investigate the triangular fuzzy multiple attribute group decision making (MAGDM) problem in which the attributes and experts are in different priority level. Motivated by the ideal of prioritized aggregation operators (R.R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 48 (2008) 263–274.), we develop some prioritized aggregation operators for aggregating triangular fuzzy information, and then apply them to develop some models for triangular fuzzy multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

A hardware-friendly arithmetic method and efficient implementations for designing digital fuzzy adders

A new hardware-friendly mathematical method for realizing low-complexity universal Adder cells as well as its efficient hardware implementations is proposed in this paper. This method can be used in binary logic, Multiple-Valued Logic (MVL) and specifically digital fuzzy systems. The proposed mathematical method can be implemented in both voltage and current modes. The voltage-mode hardware implementation is very simple and is based on input capacitors and MVL or analog inverters and buffers. In addition, the current-mode hardware implementation leads to simple and efficient structures for digital fuzzy systems. Simulations are carried out for ternary logic as well as for digital fuzzy logic with high precision by using 180 nm standard CMOS technology and at 1.8 V supply voltage. Simulation results demonstrate that the proposed designs have excellent functionality and are very suitable for implementing MVL and fuzzy arithmetic circuits.     

基于模糊决策模型的高速公路环境风险评价

本文构建了非常规突发事件下的高速公路环境风险评价的目标体系,将模糊逻辑与多目标决策、层次分析法相结合,提出一种基于模糊多目标决策模型。它与传统的多目标决策模型相比,将输入参量转化为语言变量后,获得一个集专家知识、系统变量间关系为基础的优势得分排序规则,可对不确定环境下的多目标及其子目标进行决策评价。通过设计一个高速公路路段环境评价的算例,并对其目标及子目标进行敏感性分析,验证这种决策方法在评价高速公路环境风险上的科学性。