基于可拓规则的故障诊断专家系统推理机的研究

针对传统产生式规则在知识表示、匹配冲突等方面存在的局限,提出了一种将可拓规则用于故障诊断专家系统推理机的方法;该方法重点研究了可拓规则的匹配原理和可拓推理机算法思想,提出了匹配度计算方法并用来计算故障条件与规则前件的匹配度;根据研究表明,利用可拓规则进行推理,不仅在知识表示上比传统产生式规则推理有所提高,而且还解决了传统专家系统容易出现匹配冲突等问题;最后以AMU故障推理为例,说明可拓推理机具有推理速度快、效率高等优点,取得了较好的推理效果.     

基于证据推理的确定因子规则库推理方法

不确定性推理方法是人工智能领域的一个主要研究内容,If-then规则是人工智能领域最常见的知识表示方法. 文章针对实际问题往往具有不确定性的特点,提出基于证据推理的确定因子规则库推理方法.首先在If-then规则的基础上给出确定因子结构和确定因子规则库知识表示方法,该方法可以有效利用各种类型的不确定性信息,充分考虑了前提、结论以及规则本身的多种不确定性. 然后,提出了基于证据推理的确定因子规则库推理方法. 该方法通过将已知事实与规则前提进行匹配,推断结论并得到已知事实条件下的前提确定因子;进一步,根据证据推理算法得到结论的确定因子. 文章最后,通过基于证据推理的确定因子规则库推理方法在UCI数据集分类问题的应用算例,说明该方法的可行性和高效性.     

METHODS OF REVISION PRINCIPLE FOR APPROXIMATE REASONING

Abstract

Compositional inference and compatibility-modification inference are two main approaches for approximate reasoning (Baldwin, 1979a; 1979b; Dubois, 1985; Mizumoto, 1981; Mizumoto. 1987; Tsukamoto, 1979; Yager, 1980; Zadeh, 1975a; 1975b; 1979). The former realizes inference by obtaining an implication relation between antecedent and consequent of a rule and then composing the input with the relation (Zadeh, 1975a). The latter realizes inference by determining the measure of satisfaction between input and antecedent of a rule and then using the measure to modify the rule's consequen(Dubois, 1985). The revision principle was proposed in a different way: it is under such a belief that the modification (revision) of consequent should be caused only by the difference (deviation) between input (given fact) and antecedent. In other words, when a method of revision principle is used to approximate reasoning the consequent will always be obtained as output if input is the same as the antecedent. The revising processing is based on some kind of relation between antecedent and consequent, which can be linear relation or semantic relation. We introduce five revising methods and then evaluate them by relation keeping property.     

Fuzzy arithmetic-based interpolative reasoning for nonlinear dynamic fuzzy systems

FAIR (fuzzy arithmetic-based interpolative reasoning)—a fuzzy reasoning scheme based on fuzzy arithmetic, is presented here. Linguistic rules of the Mamdani type, with fuzzy numbers as consequents, are used in an inference mechanism similar to that of a Takagi–Sugeno model. The inference result is a weighted sum of fuzzy numbers, calculated by means of the extension principle. Both fuzzy and crisp inputs and outputs can be used, and the chaining of rule bases is supported without increasing the spread of the output fuzzy sets in each step. This provides a setting for modeling dynamic fuzzy systems using fuzzy recursion. The matching in the rule antecedents is done by means of a compatibility measure that can be selected to suit the application at hand. Different compatibility measures can be used for different antecedent variables, and reasoning with sparse rule bases is supported. The application of FAIR to the modeling of a nonlinear dynamic system based on a combination of knowledge-driven and data-driven approaches is presented as an example.     

Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator

This paper presents a comparison of the two important inference schemes: “individual-rule-based inference” and “compositional rule of inference” as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.     

Belief rule-base inference methodology using the evidential reasoning Approach-RIMER

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

Inference analysis and adaptive training for belief rule based systems

Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts’ subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique.     

Combinatorial rule explosion eliminated by a fuzzy ruleconfiguration

Conventional fuzzy inference methodology relates the relevant subsets of each input universal set to the subsets of the other system inputs through an intersection-rule configuration. This strategy yields an exponential growth in the number of rules as inputs are added to the system, quickly reducing performance to unacceptable levels. A novel rule configuration and matrix design are presented in this paper that do not rely on rule multiplication to insure that antecedent elements are effectively related to their consequent counterparts. This alternative formulation models the entire system problem space with a simplified structure that increases linearly as the inference engine grows, providing significant computational savings to a broad range of commercial and scientific applications     

A trainable transparent universal approximator for defuzzificationin Mamdani-type neuro-fuzzy controllers

A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. The transparent structure of the universal defuzzification approximator allows us to analyze the generated customized defuzzification method using the existing theories of defuzzification. The integration of universal defuzzification approximator instead of traditional methods in Mamdani-type fuzzy controllers can also be considered as an addition of trainable nonlinear noise to the output of the fuzzy rule inference before calculating the defuzzified crisp output. Therefore, nonlinear noise trained specifically for a given application shows a grade of confidence on the rule base, providing an additional opportunity to measure the quality of the fuzzy rule base. The possibility of modeling a Mamdani-type fuzzy controller as a feedforward neural network with the ability of gradient descent training of the universal defuzzification approximator and antecedent membership functions fulfil the requirement known from multilayer preceptrons in finding solutions to nonlinear separable problems     

化工过程实时故障诊断专家系统的研究与开发

讨论了复杂化工过程实时故障诊断专家系统的设计与实现。对于该系统的系统结构、知识库结构以及推理机的设计进行了详细介绍。尤其是讨论了一种新的知识规则冲突消解策略——前提排序策略以及内存知识库中知识规则选择策略。并以润滑油生产过程为例,讨论了系统的故障诊断过程。实际应用表明,该系统能够及时、迅速地对故障做出响应,满足了生产需求。     

A hybrid fuzzy cognitive model based on weighted OWA operators and single‐antecedent rules

Conventional fuzzy cognitive maps (FCMs) can only represent monotonic or symmetric causal relationships and cannot simulate the AND/OR combinations of the antecedent nodes. The rule‐based fuzzy cognitive maps (RBFCMs) usually suffer from the well‐known combinatorial rule explosion problem. A hybrid fuzzy cognitive model based on weighted OWA operators and single‐antecedent rules is proposed to eliminate the drawbacks of the existing FCM models. Hybrid fuzzy cognitive maps (HFCMs) represent the causal relationships with single‐antecedent fuzzy rules and handle the various AND/OR relationships among the antecedent nodes with weighted OWA aggregation operators. Compared with conventional FCMs, HFCMs have more powerful cognitive capability. Compared with RBFCMs, HFCMs reduce the scale and complexity of the rule bases significantly and have better representation and inference performance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1189–1196, 2007.     

Rule-base structure identification in an adaptive-network-basedfuzzy inference system

We summarize Jang's architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang's basic model so that it can be used to solve classification problems by employing parameterized t-norms. We also enhance the model to include weights of importance so that feature selection becomes a component of the modeling scheme. Next, we discuss two ways of identifying system structures based on Jang's architecture: the top-down approach, and the bottom-up approach. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against input signals with logarithmic efficiency. To preserve the advantage of parallel processing assumed in fuzzy rule-based inference systems, we give a parallel algorithm for pattern matching with a linear speedup. Moreover, as we consider the communication and storage cost of an interpolation model. We propose a rule combination mechanism to build a simplified version of the original rule base according to a given focus set. This scheme can be used in various situations of pattern representation or data compression, such as in image coding or in hierarchical pattern recognition     

Ant Colony Optimization Incorporated With Fuzzy Q-Learning for Reinforcement Fuzzy Control

This paper proposes the design of fuzzy controllers by ant colony optimization (ACO) incorporated with fuzzy-Q learning, called ACO-FQ, with reinforcements. For a fuzzy inference system, we partition the antecedent part a priori and then list all candidate consequent actions of the rules. In ACO-FQ, the tour of an ant is regarded as a combination of consequent actions selected from every rule. Searching for the best one among all combinations is partially based on pheromone trail. We assign to each candidate in the consequent part of the rule a corresponding Q-value. Update of the Q-value is based on fuzzy-Q learning. The best combination of consequent values of a fuzzy inference system is searched according to pheromone levels and Q-values. ACO-FQ is applied to three reinforcement fuzzy control problems: (1) water bath temperature control; (2) magnetic levitation control; and (3) truck backup control. Comparisons with other reinforcement fuzzy system design methods verify the performance of ACO-FQ.     

An Inference Microprocessor Design

This paper is concerned with the design of an inference microprocessor for production rule systems.Its implementation is based on both exact and inexact (fuzzy logic) reasoning,so it can be used for building various production rule systems.The methods of translating linguistically expressed rules into numerical representations are described and the hardware implementations are discussed.Finally, a parallel architecture for the inference microprocessor is presented.     

On the Generalization of Single Input Rule Modules Connected Type Fuzzy Reasoning Method

Yubazaki have proposed a “single input rule modules connected type fuzzy reasoning method (SIRMs method)” in which its final output is obtained by summarizing the product of importance degree and inference result from single input fuzzy rule modules. This paper proposes a “functional-type SIRMs method,” which is an extended version of the SIRMs method, and clarifies the relationship between the Takagi--Sugeno (T--S) reasoning method and the functional-type SIRMs method. The functional-type SIRMs method can be shown to be transformed to the T--S reasoning method, but generally the conversion to functional-type SIRMs method from the T--S reasoning method is impossible. It is shown, however, that the T--S reasoning method can be transformed to the functional-type SIRMs method when the T--S reasoning method fills some conditions. For this reason, it is shown that the functional-type SIRMs method is the subset of the T--S reasoning method.      

Systematic design method of stabilization fuzzy controllers for pendulum systems

A systematic method to construct stabilization fuzzy controllers for a single pendulum system and a series-type double pendulum system is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. The angle and angular velocity of each pendulum and the position and velocity of the cart are selected as the input items. Each input item is given with a SIRM and a dynamic importance degree. All the SIRMs have the same rule setting. The dynamic importance degrees use the absolute value(s) of the angle(s) of the pendulum(s) as the antecedent variable(s). The dynamic importance degrees are designed such that the upper pendulum angular control takes the highest priority and the cart position control takes the lowest priority when the upper pendulum is not balanced upright. The control priority orders are automatically adjusted according to control situations. The simulation results show that the proposed fuzzy controllers have high generalization ability to completely stabilize a wide range of single pendulum systems and series-type double pendulum systems in short time. By extending the architecture, a stabilization fuzzy controller for a series-type triple pendulum system is even possible. © 2001 John Wiley & Sons, Inc.     

Implementation of a microcontroller-based simplified FITA-FIS model

The paper describes basic approach to building a general purpose MISO-FITA (multiple inputs single output rule based system) fuzzy logic inference system. It is also discussed classic and simplified models of the inference systems and some optimization methods of its architecture. The fuzzy engine of the proposed system is based on simplified Mamdani’s fuzzy inference model. It has been implemented on the sample platform based on ARMv7 Cortex-M4 microcontroller. The performance of the fuzzy inference system, defined as a time to obtain an output crisp inference result, is higher or comparable to another software and hardware solutions. For proposed system it even takes 10 μs.     

Tool life prediction for cup shaped cold forgings with fuzzy language risk analysis and fuzzy inference

A method of Die-life prediction is suggested for cup shaped forgings.Authors theorize that forge specialists can make die-life prediction by comparing a “target” forging process” with other standard processor whose actual life are known. The authors make comparison by calculation of risk (that which shortens die-life span). Risk is estimated by using a risk tree network, based on information compiled from a survey of forge experts. The risk rate of an “end node” is estimated by a computer-aided forging process planning system. Comparing dimensions of the target and standard forging processes has effect on the risk rate. Once risk is determined, it is used to predict dielife span using Fuzzy inference. The Fuzzy inference rule is estimated based on data received from the interview of experts.     

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. larity based approximate reasoning, an inference result is Combining the conventional compositional rule of inference with simideduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.