Narrow Results

This paper investigates the use of functions other than t-norms to model the Modus Ponens rule in a fuzzy inference process. For that purpose, new definitions for fuzzy inference related concepts are suggested, that take into account the possibility of using a larger class of functions. In particular, the concept of "Modus Ponens generating function" is revisited, allowing to find out when and where (in which subset of the defined universe) an operator is able to generate the Modus Ponens scheme. In addition, given such an operator, the conditional relations that may be used along with it to model an inference process are found. These results are applied to some common operators, finding their Modus Ponens generation capacity as well as their corresponding residuated fuzzy conditionals. Finally, the relation between an operator's ability to describe the Modus Ponens rule and its conjunctive/disjunctive behaviour is also studied, by means of a series of sufficient and/or necessary conditions relating both concepts.

Preselection of rules is an algorithm which chooses one rule from linguistic description of some process. This rule is then used for inference instead of the whole description. This article studies properties of preselection and proposes use of measures of resemblance for choosing the rule which best fits the observation.

This paper provide a formal fuzzy object inference model to solve the following four significant drawbacks identified in extant fuzzy rule-based languages. First, they have difficulty in handling composite objects as a unit of inference. Second, they don't support fuzzy reasoning which is semantically easy to understand and conceptually simple to use. Third, their knowledge representation and reasoning style have a great semantic gap with those of current database models in syntax and semantics. Finally, they do not provide a comprehensive framework in treating uncertainties. In this paper, we demonstrate that the proposed model naturally models a target application environment in terms of composite objects possibly containing uncertain information, and successfully performs a fuzzy inference between them. To practically model the environment, we use the constructs of ICOT (Integrated C-Object Tool) extended for well implementing the structural semantics of the proposed model.

Inference problems are one of the main research topics in the artificial intellect field. So far there have been various inference systems, some of them have been applied in fuzzy control according to their feature. In 1989, the concept of truth-valued flow inference was introduced by Wang¹, and its mathematical theory of truth-valued flow inference was set up by Wang² in 1995. In this paper, aimed at the real meaning of the truth-valued in the truth-valued flow inference, we introduce the concepts of the inner product truth-valued and inner product truth-valued flow inference, and analyze some inference algorithms of fuzzy control at present in detail. We reveal that the fuzzy inference algorithms in fuzzy control at present are all regarded as inner product truth-valued flow inference algorithm. Finally, inner product truth-valued flow inference approach is generalized in the multiple inputs and single output.

Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a fuzzy active object-oriented database for modeling knowledge intensive applications. In that, we incorporate fuzziness within the event, condition and action parts of an active rule. We consider deductive rules as special cases of active rules so that deductive queries are handled using abstract kind of events. We also introduce a model for fuzzy inferencing of fuzzy active rules where we develop a model for scenario concept. We use a Fuzzy Petri Net model for fuzzy rule-based inference.

The propositional product logic is one of the basic fuzzy logics with continuous t-norms, employing the product t-norm on the unit interval $[0,1]$. Our aim is to combine well-established automated deduction (theorem proving) with fuzzy inference. As a first step, we devise a modification of the procedure of Davis, Putnam, Logemann, and Loveland (DPLL) with dichotomous branching for inference in the product logic. We prove that the procedure is refutation sound and finitely complete. As a consequence, solutions to the deduction, satisfiability, and validity problems will be proposed for the finite case. The presented results are applicable to a design of intelligent systems, exploiting some kind of multi-step fuzzy inference.

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, designed to accommodate fuzzy information processing, is presented in consideration of the proposed methods.

A method of multimedia information data acquisition is proposed based on fuzzy rules, where multimedia mean the five human senses. Observed information is characterized by VAGOT (visual, acoustic, gustatory, olfactory and tactile) time series data and the goal is to extract an appropriate subset of VAGOT data based on a given instruction. Here, Multimedia Information centers on image and sound, represented by membership functions. Fuzzy rules based on visual and acoustic information are used to identify the appropriate time interval in three experiments: various cars on a general road, sunny-side-up eggs and fireworks multimedia information.

As a generalization of the compositional rule of inference (CRI) algorithm and the fully implicational algorithm, the differently implicational algorithm of fuzzy inference not only inherit the advantages of the fully implicational algorithm, but also has stronger practicability. Then, the variable differently implicational algorithm was proposed to make the current differently implicational algorithms compose a united whole. In this paper, the variable differently implicational algorithm is further researched focusing on the fuzzy modus tollens (FMT) problem. The differently implicational principle for FMT is improved. Moreover, the unified solutions of the variable differently implicational algorithm for FMT are accomplished for R- and S-implications. Following that, as an important index of fuzzy inference, the continuity of this algorithm is analyzed for main R- and S-implications, in which excellent performance is obtained. Finally, its optimal solutions as well as inference examples are provided for several specific R- and S-implications.

The emerging research area of a quantum-inspired computing has been applied to various field such as computational intelligence, and showed its superior abilities. However, most existing researches are focused on theoretical simulations, and have not been implemented in systems under practical environment. For human–robot communication, associative memory becomes essential for multi-modal communication. However, it always suffers from low memory capacity and recall reliability. In this paper, we propose a quantum-inspired bidirectional associative memory with fuzzy inference. We show that fuzzy inference satisfies basic postulates of quantum mechanics, but also learning algorithm for weight matrix in associative memory. In addition, we construct a communication system with robot partner using proposed model. This is the first successful attempt to overcome conventional problems in associative memory model with a robot application.

In order to detect and identify fire accidents accurately and efficiently, an intelligent fire identification system based on neural network algorithm is designed, which can overcome the shortcomings of single information, complex wiring, poor adaptability, etc. The characteristic extraction of sensors is adopted in the information layer to solve the problems in multi-sensor fusion. The fire data are transmitted to the main controller through LoRa wireless module and fused by back propagation neural network, which is self-learning and adaptive. The output of neural network and fuzzy inference with other factors are used for decision criteria to improve the identification accuracy. The common combustibles and various interference sources are selected for fire tests. The result shows that the detection accuracy is up to 100% and the false alarm rate is lower than 0.1%, meanwhile, the system has the advantages of fast response and high detection efficiency.

As a first step toward standardization of a practical programming language for fuzzy system applications, we proposed Fuzzy system Description Language (FDL) in 1996. The specification of the first version of FDL was not definitive edition. This specification was designed for hardware-coding of fuzzy control systems based on fuzzy inference as a prerequisite. So although it fulfills the intended functions, several problems arise for unexpected applications.

In this article, we first describe the specification of standardized FDL with its background and properties. Then, we consider some problems (the assignment operation, the comparison operation and the internal expressions, etc.) arised from wide applications of FDL. We describe the improvements of FDL. At last, we describe the fuzzy inference systems based on the indirect inference method with FDL and discuss some properties.

Repair and maintenance of existing concrete structures have become one of the most important in the field of civil engineering. Considerable experiential knowledge is required to diagnose the condition of the member and to recommend a proper repair and retrofitting procedure. Hence, there is a need for engineering computations for a proper damage assessment. This paper researches on fuzzy inference i.e., if-then rule-based program for assessment of reinforced concrete beams by considering certain parameters. This program is named as FuzDam in this paper, and it is developed using Visual Basic.

An intelligent word semantic proofing system for special domain are discussed deeply in this paper. Firstly, the fuzzy mapping aggregation rules are introduced and followed by the corresponding fuzzy inference method. Secondly, the rigid inference and the soft inference are discussed in detail. Then flowchart of the semantic analysis and the bases and the modules in the Chinese word proofing system are illustrated subsequently. The experiment shows that it is feasible and practical method for semantic analysis.

Two essential components of systems modeling are i) representation and ii) inference. We review recent developments in fuzzy systems modeling from a perspective of: a) knowledge representation with fuzzy sets including measurement and acquisition of membership functions for a system parameter identification, as well as combination of knowledge with fuzzy sets for the formation of rules in a system structure identification, and b) approximate reasoning with fuzzy logic including properties of reasoning, combination of rules and/or their consequences, and three heuristics that have been proposed during the course of development. This review is restricted to point-valued fuzzy sets and logics.