Extended triangular norms

The paper is devoted to classical t-norms extended to operations on fuzzy quantities in accordance with the generalized Zadeh extension principle. Such extended t-norms are used for calculating intersection of type-2 fuzzy sets. Analytical expressions for membership functions of some extended t-norms are derived assuming special classes of fuzzy quantities, i.e., fuzzy truth intervals or fuzzy truth numbers. The possibility of applying these results in the construction of type-2 adaptive network fuzzy inference systems is illustrated on several examples.     

Compatibility of t-norms with the concept of ?-partition

In this paper we study the behaviour of a kind of partitions formed by fuzzy sets, the ?-partitions, with respect to three important operations: refinement, union and product of partitions. In the crisp set theory, the previous operations lead to new partitions: every refinement of a partition is also a partition; the union of partitions of disjoint sets is a partition of the union set; the product of two partitions of two sets is a partition of the intersection of the partitioned sets. It has been proven that ?-partitions extend the three previous properties when the intersection of fuzzy sets is defined by the minimum t-norm and the union by the maximum t-conorm. In this paper we consider any t-norm defining the intersection of fuzzy sets and we characterize those t-norms for which refinements, unions and products of ?-partitions are ?-partitions. We pay special attention to these characterizations in the case of continuous t-norms.     

Graded logic for decision support systems

We present the soft computing graded logic (GL) as a mathematical infrastructure for a soft computing propositional calculus, and GL aggregation. The GL aggregation of degrees of truth (or fuzzy membership) is a crucial component of complex criterion functions used in decision support systems. In the context of GL we propose aggregation functions that integrate means and t-norms/conorms, combining logic and probabilistic reasoning. We also propose a set of necessary and sufficient basic logic functions (models of simultaneity, substitutability, and complementing of degrees of truth) and their analytic forms. GL is developed as a seamless soft computing generalization of classical Boolean logic. The proposed generalizations include: (1) the continuous parameterized transition from drastic conjunction to drastic disjunction, integrating the regions of hyperconjunction, hard and soft partial conjunction, logic neutrality, soft and hard partial disjunction, and hyperdisjunction and (2) semantic generalization where we assign a degree of importance to each degree of truth, in a way that is consistent with observable properties of human intuitive reasoning. These generalizations reflect the fact that degrees of truth are not anonymous real numbers, but values that have semantic identity derived from goals and requirements of decision-maker. The basic goal of GL is to provide humancentric mathematical models of logic aggregators that can be used in the decision process of comparison and selection of alternatives based on the evaluation of each alternative. This paper is structured to present the characteristic properties of entire GL in a way that is both concise and complete.     

Theoretical analysis of crisp-type fuzzy logic controllers usingvarious t-norm sum-gravity inference methods

The input-output parametric relationship of a class of crisp-type fuzzy logic controllers (FLCs) using various t-norm sum-gravity inference methods is studied. Four most important t-norms are used to calculate the matching level of each control rule and the explicit mathematical forms of reasoning surfaces obtained by using these four t-norms are addressed. Reasoning surfaces of these crisp-type FLCs are proved to be composed of a two-dimensional multilevel relay no matter which t-norm is used and a local position-dependent nonlinear compensator with output pattern influenced by the t-norms is selected. By analyzing the intrinsic operation of the four t-norms, the authors find that both standard intersection and algebraic product are suitable operators to perform the inference of the FLC. However, bounded difference and drastic intersection are disqualified because they cannot satisfy some important criteria. A measure of relative degree-of-nonlinearity is defined to examine the output figures of these crisp-type FLCs. The ultimate behavior of these crisp-type FLCs as the number of linguistic terms approaches infinity is also explored. The local stability criteria for the proportional-integral (PI)-type fuzzy control systems and the natural global stability characteristic for the proportional-derivative (PD)-type fuzzy control systems are also examined     

基于一类严格三角范数的命题逻辑

剩余模糊逻辑演算与连续三角范数是紧密相关的,三角范数是合取联结词的真值函数,三角范数的剩余是蕴涵联结词的真值函数. 在这些逻辑中,非运算都是由蕴涵和真值常量0定义的,即(→)P∶P→0-.在本文中,我们引入一种具有对合性质的强非运算联结词"～"和投影联结词"Δ",证明基于严格泛与运算模型T(x,y,h)(h∈(0.75,1))的命题演算逻辑PC(T)系统是基本严格模糊逻辑SBL;PC(T)～是基本严格模糊逻辑SBL的扩张SBL～.     

Parametric classes of generalized conjunction and disjunctionoperations for fuzzy modeling

It is argued that inference procedures of fuzzy models do not always require commutativity and associativity of the operations used. This raises the possibility of considering nonassociative and noncommutative conjunction and disjunction operations. Such operations are investigated in this paper and different methods for their generation are proposed. A number of new types of conjunction operations that are simpler than the known parametric classes of T-norms are given and, as an application example, the approximation of a function by a fuzzy inference system is considered     

Fuzzy modeling based on generalized conjunction operations

An approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of the new conjunction operations, are given and their approximation performances are compared with the approaches based on a tuning of membership functions and other approaches proposed in the literature. It is seen that the tuning of the conjunction operations can be used for obtaining fuzzy models with a sufficiently good performance when the tuning of membership functions is not possible or not desirable.     

New triangular operator generators for fuzzy systems

Triangular operators (t-operators) form an integral part in the design and analysis of fuzzy systems. Simple monotonic, continuous, nonconditional functions are used in an operator generator to generate t-operators. Depending on the operator generator and the function that it uses, it becomes easier to characterize and classify the families of t-operators. In this paper, the author proposes two operator generators that will extend the domain of triangular operators in the realm of fuzzy set theory. The conventional operator generators generate a t-norm and a t-conorm by using a decreasing function and an increasing function, respectively. In contrast, in this study, increasing functions generate t-norms, while decreasing functions generate t-conorms, respectively     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

Implication operators in fuzzy logic

The choice of fuzzy implication as well as other connectives is an important problem in the theoretical development of fuzzy logic, and at the same time, it is significant for the performance of the systems in which fuzzy logic technique is employed. There are mainly two ways in fuzzy logic to define implication operators: (1) an implication operator is considered as the residuation of conjunction operator; and (2) it is directly defined in terms of negation, conjunction, and disjunction operators. The purpose of this paper is to determine the number of implication operators defined in the second way for some usual negation, conjunction and disjunction operators in fuzzy logic     

On prioritized weighted aggregation in multi-criteria decision making

This paper deals with multi-criteria decision making (MCDM) problems with multiple priorities, in which priority weights associated with the lower priority criteria are related to the satisfactions of the higher priority criteria. Firstly, we propose a prioritized weighted aggregation operator based on ordered weighted averaging (OWA) operator and triangular norms (t-norms). To preserve the tradeoffs among the criteria in the same priority level, we suggest that the degree of satisfaction regarding each priority level is viewed as a pseudo criterion. On the other hand, t-norms are used to model the priority relationships between the criteria in different priority levels. In particular, we show that strict Archimedean t-norms perform better in inducing priority weights. As Hamacher family of t-norms provide a wide class of strict Archimedean t-norms ranging from the product to weakest t-norm, Hamacher parameterized t-norms are used to induce the priority weight for each priority level. Secondly, considering decision maker (DM)’s requirement toward higher priority levels, a benchmark based approach is proposed to induce priority weight for each priority level. In particular, ?ukasiewicz implication is used to compute benchmark achievement for crisp requirements; target-oriented decision analysis is utilized to obtain the benchmark achievement for fuzzy requirements. Finally, some numerical examples are used to illustrate the proposed prioritized aggregation technique as well as to compare with previous research.     

Fuzzy additive hybrid operators for network-based decision making

Evidence Aggregation Networks based on multiplicative fuzzy hybrid operators were introduced by Krishnapuram and Lee. They have been used for image segmentation, pattern recognition, and general multicriteria decision making. One of the drawbacks to these networks is that the training is complex and quite time consuming. In this article, we modify these aggregation networks to implement additive fuzzy hybrid connectives. We study the theoretical properties of two classes of such aggregation operators, one where the union and intersection components are based on multiplication, and the other where these components are derived from Yager connectives. These new networks have similar excellent properties such as backpropagation training and node interpretability for decision making under uncertainty as do their multiplicative precursors. They also have the advantage that training is easier since the derivatives of the additive hybrid operators are not as complex in form. the appropriate training algorithms are derived, and several examples given to illustrate the properties of the networks. © 1994 John Wiley & Sons, Inc.     

软BCK代数再研究

给出了软BCK代数的广义交运算和广义并运算,证明了一个集合X上的一族软BCK代数{(F_i,A_i)}_#em/em#∈I的广义交和一族软BCK代数{(F_i,A_i)}_#em/em#∈I（对任意#em/em#,j∈I,#em/em#≠j有A_i∩A_j=?)的广义并仍然是X上的软BCK代数,集合X上的软BCK代数(G,B)的一族软子代数的交、广义交和广义并仍是(G,B)的软子代数。     

Toward complex fuzzy logic

Complex fuzzy logic is a postulated logic system that is isomorphic to the complex fuzzy sets recently described in a previous paper. This concept is analogous to the many-valued logics that are isomorphic to type-1 fuzzy sets, commonly known as fuzzy logic. As with fuzzy logics, a complex fuzzy logic would be defined by particular choices of the conjunction, disjunction and complement operators. In this paper, an important assertion from a previous paper, that only the modulus of a complex fuzzy membership should be considered in set theoretic (or logical) operations, is examined. A more general mathematical formulation (the property of rotational invariance) is proposed for this assertion, and the impact of this property on the form of complex fuzzy logic operations is examined. All complex fuzzy logics based on the modulus of a vector are shown to be rotationally invariant. The case of complex fuzzy logics that are not rotationally invariant is examined using the framework of vector logic. A candidate conjunction operator was identified, and the existence of a dual disjunction was proven. Finally, a discussion on the possible applications of complex fuzzy logic focuses on the phenomenon of regularity as a possible fuzzification of stationarity.     

Evidence aggregation networks for fuzzy logic inference

Fuzzy logic has been applied in many engineering disciplines. The problem of fuzzy logic inference is investigated as a question of aggregation of evidence. A fixed network architecture employing general fuzzy unions and intersections is proposed as a mechanism to implement fuzzy logic inference. It is shown that these networks possess desirable theoretical properties. Networks based on parameterized families of operators (such as Yager's union and intersection) have extra predictable properties and admit a training algorithm which produces sharper inference results than were earlier obtained. Simulation studies corroborate the theoretical properties.     

Nonstationary Fuzzy Sets

In this paper, the notion termed a “nonstationary fuzzy set” is introduced, and the concept of a perturbation function that is used for generating nonstationary fuzzy sets is presented. Definitions of the basic set operators (the union, the intersection, and the complement) for nonstationary fuzzy sets are given, together with proofs of selected properties of these operators. Two case studies were carried out in order to illustrate the use of nonstationary fuzzy sets in a nonstationary fuzzy inference, and to provide an initial insight into the relationships between nonstationary and interval type-2 fuzzy sets.      

Modifiers based on some t-norms in fuzzy logic

Modifiers generated by n-placed functions are considered. The subject matter of modifiers are fuzzy sets, i.e. membership functions defined on the unit interval I = [0, 1]. Two sets of modifiers are considered as example cases. One of them is a set of modifiers generated by t-norms and t-conorms. Here different dual pairs of norms create modifiers of different grade of strength. These norms are examples of two-placed functions. Another case is to generate a series of modifiers using only one DeMorgan class of norms. Norms are generalized to be n-placed functions. The place number n takes effect to the strength of a modifier. Two different DeMorgan classes are taken into the consideration. The first steps to the direction of many-valued modifier logics are taken.     

Operations on fuzzy sets—an axiomatic approach

We introduce intersection and union of fuzzy sets, constructed by means of operations on the unit interval which may depend on the considered element of the universe of discourse. A similar approach for complementation was given by Lowen [7]. The main result is that element-dependent operations are not compatible with the preservation of the intersection and union under the inverse image of a function. Finally we consider fuzzy σ-algebras built with these set-theoretic operations.     

Characterization of co-observable languages and formulas for their super/sublanguages

We present fixed-point based characterization of several classes of co-observable languages that are of interest in the context of decentralized supervisory control of discrete-event systems, including C&P /spl or/ D&A co-observable languages, C&P co-observable languages, and D&A co-observable languages. We also provide formulas for computing super/sublanguages for each of these classes. In cases where the class of co-observable languages is not closed under intersection/union, we provide upper/lower bound of the super/sublanguage formula we present. The computation of super/sublanguages and also computation of their upper/lower bounds has lead to the introduction of other classes of co-observable languages, namely, strongly C&P co-observable languages, strongly D&A co-observable languages, locally observable languages, and strongly locally observable languages. Fixed-point based characterization of all the above language classes is also given, and their closure under intersection/union is investigated. We also study whether the fixed-point operator preserves prefix closure, relative closure (also called L/sub m/(G)-closure), and controllability.     

On the representation of intuitionistic fuzzy t-norms and t-conorms

Intuitionistic fuzzy sets form an extension of fuzzy sets: while fuzzy sets give a degree to which an element belongs to a set, intuitionistic fuzzy sets give both a membership degree and a nonmembership degree. The only constraint on those two degrees is that their sum must be smaller than or equal to 1. In fuzzy set theory, an important class of triangular norms and conorms is the class of continuous Archimedean nilpotent triangular norms and conorms. It has been shown that for such t-norms T there exists a permutation /spl phi/ of [0,1] such that T is the /spl phi/-transform of the Lukasiewicz t-norm. In this paper we introduce the notion of intuitionistic fuzzy t-norm and t-conorm, and investigate under which conditions a similar representation theorem can be obtained.