Fuzzy inference in rule-based real-time control

In this paper research into the application of ‘expert system’-like inference mechanisms in the field of fuzzy control is adressed. Using techniques from the area of rule-based expert systems, a more flexible way of design and modification is presented of new and existing fuzzy systems for modelling and control. In comparison with ‘normal’ applications of fuzzy inference, the ćompositional rule of inference is replaced by a fuzzy inference engine. General applicability of the fuzzy inference engine is made possible by its general character as a fuzzy expert system shell. Succesful implementations in simulation and realtime control environments show the flexibility and usefullness of the described fuzzy inference engine.     

Designing fuzzy inference systems from data: Aninterpretability-oriented review

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined     

Some applications of fuzzy logic in rule–based expert systems

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.     

一类提取模糊规则的新方法及其在干燥建模中的应用

基于系统的输入－输出数据,提出一种通过划分输入空间撮模糊规则的方法,并将该方法应用于建模非线性程度较高物干燥过程。建模过程中,首先利用训练样本将输入空间动态地划分为若干个子空间,然后针对第一个子空间了生一条模糊规则,再将所产生的模糊规则共成一个模糊规则库,模糊逻辑系统的最终输出从模糊规则库中产生。仿真结果表明,该方法可很好地预测干燥系统的物料降水率,而且简单实用,十分可靠。     

基于着色Petri网模糊专家系统的研究

针对变电站无功控制模糊专家系统知识表示不确定性及规则数量多的特点,文章以模糊、着色Petri网为基础,提出了一种基于模糊着色Petri网的知识表示与规则获取方法。该方法利用Petri网的图形化环境特点,将模糊规则库的不同变量用不同的颜色加以区分,不同规则中的同一个变量用该变量的颜色集表示,构成一个模糊着色Petri网模型。充分利用着色Petri网的特点,对推理过程进行了仔细研究,并提出一种基于着色模糊Petri网的启发式搜索策略。将其用于变电站无功控制的模糊专家系统中,结果表明,基于着色Petri网的模糊知识表示和获取方法,对于大型、复杂变电站模糊专家控制系统是非常有效的。     

Nutrient monitoring,simulation and management within a major lowland UK river system: the Kennet

This paper describes an assessment of the nitrogen and phosphorus dynamics of the River Kennet in the south east of England. The Kennet catchment (1200 km²) is a predominantly groundwater fed river impacted by agricultural and sewage sources of nutrient (nitrogen and phosphorus) pollution. The results from a suite of simulation models are integrated to assess the key spatial and temporal variations in the nitrogen (N) and phosphorus (P) chemistry, and the influence of changes in phosphorous inputs from a Sewage Treatment Works on the macrophyte and epiphyte growth patterns. The models used are the Export Co-efficient model, the Integrated Nitrogen in Catchments model, and a new model of in-stream phosphorus and macrophyte dynamics: the ‘Kennet’ model. The paper concludes with a discussion on the present state of knowledge regarding the water quality functioning, future research needs regarding environmental modelling and the use of models as management tools for large, nutrient impacted riverine systems.     

A fuzzy neural network with fuzzy impact grades

Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge. This creates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood (MSBFNN) and its fuzzy rule identification algorithms. In our approach, fuzzy rules are described by different fuzzy sets. For each fuzzy set representing a fuzzy rule, the universe of discourse is defined as the summation of weighted membership grades of input linguistic terms that associate with the given fuzzy rule. In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules. Afterwards, the proposed fuzzy neural network quantifies the impacts of fuzzy rules on the consequent parts by fuzzy connections based on mutual subsethood. Furthermore, to enhance the knowledge representation and interpretation of the rules, a linear transformation from consequent parts to output is incorporated into MSBFNN so that higher accuracy can be achieved. In the parameter identification phase, the backpropagation algorithm is employed, and proper linear transformation is also determined dynamically. To demonstrate the capability of the MSBFNN, simulations in different areas including classification, regression and time series prediction are conducted. The proposed MSBFNN shows encouraging performance when benchmarked against other models.     

调整模糊控制规则的规则表旋转法

提出了用规则表旋转法修改控制规则的方法. 模糊控制器通常是基于专家经验或对象模型而设计的. 无论模糊模型或是经验都是粗糙和不能令人满意的. 提出的方法可以通过旋转规则表来调整原始的规则. 仿真结果说明, 这个方法是一种在模糊控制系统中有效调整规则的方法.     

Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery

The objective of this study is to design a fuzzy expert system for performance assessment of health, safety, environment (HSE) and ergonomics system factors in a gas refinery. This will lead to a robust control system for continuous assessment and improvement of HSE and ergonomics performance. The importance of this study stems from the current lack of formal integrated methodologies for interpreting and evaluating performance data for HSE and ergonomics. Three important reasons to use fuzzy expert systems are (1) reduction of human error, (2) creation of expert knowledge and (3) interpretation of large amount of vague data. To achieve the objective of this study, standard indicators and technical tolerances for assessment of HSE and ergonomics factors are identified. Then, data is collected for all indicators and consequently, for each indicator four conditions are defined as “acceptance”, “low deviation”, “mid deviation” and “high deviation”. A membership function is defined for each fuzzy condition (set) because an indicator cannot be allocated to just one of the above conditions. The expert system uses fuzzy rules, which are structured with Data Engine. Previous studies have introduced HSE expert system whereas this study introduces an integrated HSE and ergonomics expert system through fuzzy logic.     

Evolution of a Negative-Rule Fuzzy Obstacle Avoidance Controller for an Autonomous Vehicle

A fuzzy obstacle avoidance controller is designed for an autonomous vehicle. The controller is given the capability for obstacle avoidance by using negative fuzzy rules in conjunction with traditional positive ones. Negative fuzzy rules prescribe actions to be avoided rather than performed. A rule base of positive rules is specified by an expert for directing the vehicle to the target in the absence of obstacles, while a rule base of negative rules is experimentally determined from expert operation of the vehicle in the presence of obstacles. The consequents of the negative-rule system are codified into a chromosome, and this chromosome is evolved using an evolutionary algorithm. The resulting fuzzy system has far fewer rules than would be necessary for an obstacle avoidance controller using purely positive rules, while in addition retaining greater interpretability.     

Towards a theory of fuzzy causality for diagnosis

Parsimonious covering offers an alternative to rules for building diagnostic expert systems. Abductive paradigms, such as parsimonious covering, are a departure from the forward-chaining, rule-based approach, which is based on deduction. Parsimonious covering addresses weaknesses of rule-based systems where the diagnosis may contain multiple faults or disorders, or where the need to include all the necessary context for each rule's application in the antecedent clauses of each rule would make the representation of the knowledge base too large or overly complex.

In this paper, we compare the notions of deterministic covering and the probabilistic causal model with two fuzzy analogies: fuzzy subsethood and fuzzy similarity. Monotonic upper and lower bounds for fuzzy similarity are derived, and pruning opportunities are identified for search through the power set of disorders, given a measured, crisp manifestation set.     

Generic form feature recognition and operation selection using connectionist modelling

For the purpose of enhancing the adaptability of computer-aided process planning systems, two connectionist modelling methods, namely neocognitron (i.e. neural network modelling for pattern recognition) and fuzzy associative memories (FAM), are applied to the phases of feature recognition and operation selection respectively in order to provide the system with the ability of self-learning and the ability to integrate traditional expert planning systems with connectionism-based models. In this paper, the attributed adjacency graph (AAG) extracted from a (B-Rep) solid model is converted to attributed adjacency matrices (AAM) that can be used as input data for the neocognitron to train and recognize feature patterns. With this technique, the system can not only self-reconstruct its recognition abilities for new features by learning without a priori knowledge but can also recognize and decompose intersection features. A fuzzy connectionist model, which is created using the Hebbian fuzzy learning algorithm, is employed subsequently to map the features to the appropriate operations. As the algorithm is capable of learning from rules, it is much easier to integrate the proposed model with conventional expert CAPP systems so that they become more generic in dealing with uncertain information processing and perform knowledge updating. mg]Keywords mw]Computer-aided process planning mw]feature recognition mw]neural networks mw]fuzzy neural networks mw]operation selection mw]connectionist model mw]fuzzy associative memories     

Evolutionary design of a fuzzy classifier from data

Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.     

A generalised fuzzy reasoning algorithm for an object-oriented expert system tool

Abstract: A fuzzy reasoning algorithm is presented in this paper. It is based on the concept of a generalised fuzzy production rule that adds new conjunctions such as ADD and REL to the usual AND and OR conjunctions for the linkage of premises in conventional rules. A definition is given for weights and related factors, to express and measure a relation between the premises. A formal representation for fuzzy premises is presented, together with the introduction of a fuzzy match method. Multiple thresholds are used to convert the uncertainty measures of reasoning results into linguistic variables. We have applied the fuzzy reasoning algorithm to a frame-based hybrid expert system tool. An object-oriented approach is used in implementing the system. Some results of the application are presented and discussed.     

炼油厂常压塔侧线质量实时监测模糊专家系统

通过对炼油厂分馏塔侧线质量进行机理分析，并根据实际流程利用ＡＳＰＥＮ对过程进行模拟得到各控制变量与侧线质量的对应结果，利用数据统计方法，得到带有可信度ＣＦ量度的规则。利用正向推理，构置了一个基于规则在线监测炼油厂分馏塔侧线质量指标的模糊专家系统ＳＱＰＥＳ，并利用工厂采集的实时数据对所建立的专家系统进行了验证。结果表明，这种规则获取手段对建立炼油厂分馏塔质量指标监测的专家系统是适用的，它的建立，即可     

Research and Design of a Fuzzy Neural Expert System

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.     

Rule chaining in fuzzy expert systems

A fuzzy expert system must do rule chaining differently than a nonfuzzy expert system. In particular, any rule that can fire with a particular linguistic variable in its consequent must fire before any rule whose antecedent conditions depend upon the resultant fuzzy set value of the consequent linguistic variable is allowed to fire. The dependent rules would be considered in a chain with the fuzzy rules which generate or assert the needed fuzzy linguistic variable. A recent paper by J. Pan et al. (1998) points out that a version of the FuzzyCLIPS expert system shell does not operate with chained fuzzy rules as one would expect. They introduce FuzzyShell which is described as the only known shell to have the expected fuzzy rule chaining performance. We show several approaches to obtaining the desired behavior in FuzzyCLIPS. Further, a potential pitfall with the FuzzyShell approach to dealing with chaining is pointed out     

Diagnosis of the diseases--using a GA-fuzzy approach

The objective of our study is to design an expert system by modelling the knowledge and thinking process of a doctor. A fuzzy logic controller (FLC) is used to model the process and a genetic algorithm (GA) helps to select a number of good rules from a manually constructed large rule base of an FLC, based on the opinion of 10 doctors. The GA-based tuning is done off-line. Once the optimized rule base of the FLC is obtained, it can diagnose the disease, on-line. The scope of the present work has been extended to two diseases, namely Pneumonia and Jaundice. The symptoms of each disease are fed as inputs to the FLC and the output, i.e., grade of a disease is determined.     

A fuzzy Petri net-based expert system and its application to damageassessment of bridges

In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfil the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.