多重广义MP问题的三I真度解

在二值逻辑系统中基于真度理论讨论了三I推理机制的意义,求出了真度理论下的多重广义MP问题的三I解,证明了该解与其形式解是等价的解,并推广得到了多重广义MP问题的α-三I真度解。     

多重广义MP问题的三I真度解

在二值逻辑系统中基于真度理论讨论了三I推理机制的意义,求出了真度理论下的多重广义MP问题的三I解,证明了该解与其形式解是等价的解,并推广得到了多重广义MP问题的α-三I真度解。     

广义MP问题的三I真度解

基于真度理论讨论了三I推理机制在真度理论下的意义,求出了真度理论下的广义MP问题的三I解,证明了该解与其形式解是等价的解,并推广得到了广义MP问题的α-三I真度解。     

一种基于真度变换观点的近似推理

以全新的思想和视角,把蕴涵式p→q看作一种真度变换,并提出了真度变换率和真度变换差的概念,然后在此基础上给出了一组称为肯定前件式和否定后件式真度假言推理的推理规则,从而得到了一种命题近似推理的新方法。把该方法推广到谓词逻辑,就得到一种基于谓词逻辑的近似推理新方法。因此,文章的思想和方法可作为模糊推理的理论基础。     

Triple I method of fuzzy reasoning

The theory of the triple I method with total inference rules of fuzzy reasoning is investigated by using Zadeh's implication operator R_z. The computational formulae for both fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) are obtained. The reversibility properties for FMP and FMT are analyzed and the reversibility criteria are given. We also investigated the generalized problem of the triple I method and obtained the formulae for the -triple I FMP and the -triple I FMT.     

Unified forms of Triple I method

First, the present paper provides unified forms of Triple I method for fuzzy modus ponens and fuzzy modus tollens of which diverse implication operators can be employed. Second, it is clarified that, in a sense, Zadeh's CRI method for fuzzy modus ponens can be brought into line with the unified form of the Triple I method. Lastly, a unified form of α-Triple I method is established as well, and a duality result concerning α-Triple I solutions of fuzzy modus ponens and fuzzy modus tollens is obtained.     

Extension of O-theory to problems of logical inferencing

This article extends Operator-Uncertainty Theory (OT) to the problem of uncertainty propagation in logical inferencing systems. the OT algebra and propositional interpretations presented in previous articles are applied here to derive operators for logical inferencing in the presence of conflict and undecidability. Operators for propagating uncertainties through the logical operations of disjunction and conjunction are defined. In addition, new OT operators for implication, modus ponens and modus tollens, are also proposed. the operators derived using the OT methodology are found to give rise to a four-valued logic similar to that used in computer circuit design. This framework allows uncertainty in inferencing to be represented in the form of rules convenient for use in expert systems as well as logical networks. the theory is general enough to deal with questions of conflict and undecidability, and to propagate their effects through the most widely used inference operations.     

基于支持度理论的广义Modus Ponens问题的最优解

为了将模糊推理纳入逻辑的框架并从语构和语义两个方面为模糊推理奠定严格的逻辑基础,通过将模糊推理形式化的方法移植到经典命题逻辑系统中,把FMP(fuzzy modus ponens)问题转化为GMP(generalized modus ponens)问题,并基于公式的真度概念提出了公式之间的支持度,进一步利用支持度的思想引入了GMP问题以及CGMP(collective generalized modus ponens)问题的一种新型最优求解机制.证明了最优解的存在性,同时指出,在经典命题逻辑系统中存在着与模糊逻辑完全相似的推理机制.该方法是一种程度化的方法,这就使得求解过程从算法上实现成为可能,并对知识的程度化推理有所启示.     

A neural fuzzy system with fuzzy supervised learning

A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.     

Reverse triple Ⅰ method of fuzzy reasoning

A theory of reverse triple I method with sustention degree is presented by using the implication operator R0 in every step of the fuzzy reasoning. Its computation formulas of supremum for fuzzy modus ponens and infimum for fuzzy modus tollens are given respectively. Moreover, through the generalization of this problem, the corresponding formulas of α-reverse triple I method with sustention degree are also obtained. In addition, the theory of reverse triple I method with restriction degree is proposed as well by using the operator R0, and the computation formulas of infimum for fuzzy modus ponens and supremum for fuzzy modus tollens are shown.     

多重多维模糊推理算法的连续性和逼近性

针对多重、多维模糊推理情形,细致地研究了几类模糊推理算法是否满足连续性和逼近性,并进一步讨论了这几类算法对逼近误差的传播性能。把模糊推理算法看成是一个模糊集合到另一个模糊集合的映射,选用海明距离作为两模糊集的距离度量方法,证明了在模糊假言推理和模糊拒取式推理情形,几类多重多维模糊算法都拥有连续性。当多重多维模糊算法满足还原性时就具有逼近性;该模糊算法都不会放大逼近误差。结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用。     

两类模糊推理算法的连续性和逼近性

对Zadeh的模糊推理合成法则(CRI算法)和全蕴涵三I算法(三I算法)是否满足连续性和逼近性问题进行了细致的研究,进一步讨论了这两类算法对逼近误差的传播性能.为此,把模糊推理算法看成是模糊集合到模糊集合的映射,选用海明距离作为两模糊集的距离.证明了在模糊假言推理和模糊拒取式推理情形,这两类算法都拥有连续性.指出三I算法在已知规则的前件和后件是正规集的条件下总是满足逼近性,而CRI算法只有当它满足还原性时才拥有逼近性.在满足逼近性的条件下,两类算法都不会放大逼近误差.结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用.     

Mamdani模糊推理算法的直觉化扩展

对Mamdani模糊推理算法进行了直觉化扩展。首先将Mamdani定义的模糊关系R_c进行直觉化扩展;然后推出了其对应的直觉模糊取式推理算法和直觉模糊拒式推理算法;最后以具体算例叙述了推理计算过程中的细节,验证了该方法的正确性和有效性依据直觉准则对其性能进行了评价。     

Reconstructing Popov v. Hayashi in a framework for argumentation with structured arguments and Dungean semantics

In this article the argumentation structure of the court??s decision in the Popov v. Hayashi case is formalised in Prakken??s (Argument Comput 1:93?C124; 2010) abstract framework for argument-based inference with structured arguments. In this framework, arguments are inference trees formed by applying two kinds of inference rules, strict and defeasible rules. Arguments can be attacked in three ways: attacking a premise, attacking a conclusion and attacking an inference. To resolve such conflicts, preferences may be used, which leads to three corresponding kinds of defeat, after which Dung??s (Artif Intell 77:321?C357; 1995) abstract acceptability semantics can be used to evaluate the arguments. In the present paper the abstract framework is instantiated with strict inference rules corresponding to first-order logic and with defeasible inference rules for defeasible modus ponens and various argument schemes. The main techniques used in the formal reconstruction of the case are rule-exception structures and arguments about rule validity. Arguments about socio-legal values and the use of precedent cases are reduced to arguments about rule validity. The tree structure of arguments, with explicit subargument relations between arguments, is used to capture the dependency relations between the elements of the court??s decision.     

A neural fuzzy system with linguistic teaching signals

A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use α-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, two kinds of learning schemes are developed for the proposed system: fuzzy supervised learning and fuzzy reinforcement learning. Simulation results are presented to illustrate the performance and applicability of the proposed system     

Issues on adjointness in multiple-valued logics

We contribute to the theory of implications and conjunctions related by adjointness, in multiple-valued logics. We suggest their use in Zadeh’s compositional rule of inference, to interpret generalized modus ponens inference schemata. We provide new complete characterizations of implications that distinguish left arguments, implications that satisfy the exchange principle, divisible conjunctions, commutative conjunctions, associative conjunctions and triangular norms. We also introduce and characterize pseudo-strict and pseudo-continuous implications and conjunctions, and we explore the close relationship between these two notions.     

Adaptive Fuzzy Neural Networks as identifiers of discrete-time nonlinear dynamic systems

An adaptive supervised learning scheme is proposed in this paper for training Fuzzy Neural Networks (FNN) to identify discrete-time nonlinear dynamical systems. The FNN constructs are neural-network-based connectionist models consisting of several layers that are used to implement the functions of a fuzzy logic system. The fuzzy rule base considered here consists of Takagi-Sugeno IF-THEN rules, where the rule outputs are realized as linear polynomials of the input components. The FNN connectionist model is functionally partitioned into three separate parts, namely, the premise part, which provides the truth values of the rule preconditional statements, the consequent part providing the rule outputs, and the defuzzification part computing the final output of the FNN construct. The proposed learning scheme is a two-stage training algorithm that performs both structure and parameter learning, simultaneously. First, the structure learning task determines the proper fuzzy input partitions and the respective precondition matching, and is carried out by means of the rule base adaptation mechanism. The rule base adaptation mechanism is a self-organizing procedure which progressively generates the proper fuzzy rule base, during training, according to the operating conditions. Having completed the structure learning stage, the parameter learning is applied using the back-propagation algorithm, with the objective to adjust the premise/consequent parameters of the FNN so that the desired input/output representation is captured to an acceptable degree of accuracy. The structure/parameter training algorithm exhibits good learning and generalization capabilities as demonstrated via a series of simulation studies. Comparisons with conventional multilayer neural networks indicate the effectiveness of the proposed scheme.     

剩余型直觉模糊推理的反向三I方法

提出直觉模糊取式(IFMP)和直觉模糊拒取式(IFMT)问题的直觉模糊推理的反向三I原则、反向α-三I原则和反向三I约束原则.针对剩余型直觉模糊蕴涵算子,给出IFMP、IFMT问题的直觉模糊推理的反向三I算法、反向α-三I算法及反向三I约束算法求解的计算公式和分解形式,指出这些算法都是相应模糊集情形下的推广,讨论IFMP、IFMT问题的直觉模糊推理的反向三I算法的还原性.     

Averaging linguistic truth values in fuzzy approximate reasoning

The properties of a new rule for fuzzy conditional inference are presented and discussed. The rule is based on the extended mean operator defined on fuzzy numbers. The related propositions have the form “X is A is τ,” where τ is an element of the term set of the linguistic variable truth. The results obtained via the rule match with Fukami's and with the critical analysis carried out by Mizumoto and Zimmermann about the generalized modus ponens. © 1998 John Wiley & Sons, Inc.