Applications of a novel fuzzy expert system shell

Abstract: Much of the information resident in the knowledge base of a typical expert system is imprecise, incomplete or not totally reliable. The special features of a novel expert system shell based on fuzzy logic and numbers are presented. This rule-based system can deal with exact, inexact (fuzzy) and combined reasoning as well as uncertainty represented by fuzzy numbers. Natural language interface is built in naturally using fuzzy logic representation. Several application areas, namely, classification, risk analysis and information retrieval, are discussed with four appropriate sample expert systems actually built using this shell. Through these expert systems, the additional power and advantages over traditional expert systems are illustrated. It has been demonstrated that the introduction of fuzzy concepts into expert systesms is not feasible but highly desirable.     

Analysis of assembly operations difficulty: a fuzzy expert system approach

Typical mechanical products can be assembled in various sequences of assembly operations. These sequences have high impact on the assembly time, machine utilization and even on the product quality. In order to select the best sequence of assembly operations, proper evaluation of the various sequences of assembly operations is required. This, in turn, requires the consistent evaluation of each assembly operation in the sequence. The assembly operations can be evaluated for various criteria, of which the operation difficulty is the most meaningful. This paper describes a methodology to analyse the assembly operations and calculates an operation's degree of difficulty using an expert system. This analysis consists of two steps: the first one identifies the main parameters that affect the assembly difficulty and assigns fuzzy triangular values to these parameters. The second step assigns weights to the parameters in order to maximize the agreement with a domain expert. The expert system analyses the difficulty of the assembly operation performed in two orientations: horizontal and vertical. The expert system then assigns a triangular fuzzy number as the aggregate measure of the operation's difficulty.     

A practical approach to a process planning expert system for manufacturing processes

In this paper the methodical techniques applied by the human process planning expertise is simulated. It considers the process plans design, or process selection. Software modules are designed to generate a process plan or several plans for a new part according to the input data from its engineering drawing. A specific module for each surface type, to match the surface parametric data and the required quantities with respect to the capability matrices, in order to locate the most eligible process plan is identified and used.     

A semantic fuzzy expert system for a fuzzy balanced scorecard

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF–THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.     

A genetic fuzzy expert system for automatic question classification in a competitive learning environment

Intelligent tutoring systems are efficient tools to automatically adapt the learning process to the student’s progress and needs. One of the possible adaptations is to apply an adaptive question sequencing system, which matches the difficulty of the questions to the student’s knowledge level. In this context, it is important to correctly classify the questions to be presented to students according to their difficulty level. Many systems have been developed for estimating the difficulty of questions. However the variety in the application environments makes difficult to apply the existing solutions directly to other applications. Therefore, a specific solution has been designed in order to determine the difficulty level of open questions in an automatic and objective way. This solution can be applied to activities with special temporal and running features, as the contests developed through QUESTOURnament, which is a tool integrated into the e-learning platform Moodle. The proposed solution is a fuzzy expert system that uses a genetic algorithm in order to characterize each difficulty level. From the output of the algorithm, it defines the fuzzy rules that are used to classify the questions. Data registered from a competitive activity in a Telecommunications Engineering course have been used in order to validate the system against a group of experts. Results show that the system performs successfully. Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.     

A fuzzy expert system approach for real-time monitoring of endemic diseases

In this paper, an architecture of LEPDIAG—a knowledge based system for on-line diagnosis and for monitoring prognosis of leprosy—is presented. The important features of LEPDIAG that have been detailed are a multiple expert environment, a homeostatic expert containing the model of immune reaction, a performance evaluator that can compare the observed signs and symptoms with those predicted by homeostatic expert, and a prognostic expert which optimizes the management schedule for the patients. The entire system is built around a fuzzy expert system building tool FRUIT to deal with the imprecise knowledge.     

An expert system for scheduling in a flexible manufacturing system

The use of artificial intelligence techniques has been investigated as a contribution to a solution to the following problems: daily estimated scheduling in a FMS, and real time control of production disturbances.We present our investigation in object oriented languages to represent the relevant information of a scheduling problem, particularly knowledge about constraints and flexibility factors in a shop. We detail the off-line daily scheduling prototype SOJA and the reactive capabilities we are incorporating in it to repair previous schedules with respect to real-time situations. The integration of automatic generation and modification of schedule plans leads us to introduce a coordination level which generates an admissible solution and a local, cooperation level which deals with local modifications of the plan.     

Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism.     

Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

This paper presents a systematic Type-II fuzzy expert system for diagnosing the human brain tumors (Astrocytoma tumors) using T₁-weighted Magnetic Resonance Images with contrast. The proposed Type-II fuzzy image processing method has four distinct modules: Pre-processing, Segmentation, Feature Extraction, and Approximate Reasoning. We develop a fuzzy rule base by aggregating the existing filtering methods for Pre-processing step. For Segmentation step, we extend the Possibilistic C-Mean (PCM) method by using the Type-II fuzzy concepts, Mahalanobis distance, and Kwon validity index. Feature Extraction is done by Thresholding method. Finally, we develop a Type-II Approximate Reasoning method to recognize the tumor grade in brain MRI. The proposed Type-II expert system has been tested and validated to show its accuracy in the real world. The results show that the proposed system is superior in recognizing the brain tumor and its grade than Type-I fuzzy expert systems.     

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.     

Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system

Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with intelligent agents to enable a system to have autonomy of decision making, sociability to interact with other systems, and intelligence to learn dynamically changing environments. In the proposed system, machines with intelligent agents evaluate the priorities of jobs and distribute them through negotiation. In addition, we propose methods for machines with intelligent agents to learn to make better decisions. The performance of the proposed system and the dispatching rule is demonstrated by comparing the results of the scheduling problem with early completion, productivity, and delay. The obtained results show that the manufacturing system with distributed artificial intelligence is competitive in a dynamic environment.     

An expert system for manufacturing cell control

An expert control system was designed to control an unmanned manufacturing cell in order to meet the operational requirements of a cellular Manufacturing System (CMS). In this paper, a knowledge-based three-layer control concept was used to build the cell control system. This cell control system is built to include workers' experience and problem handling ability. The cell control algorithms and heuristics are based on the pull system control principle. A Petri net is used to generate the cell control algorithm. The structure of the control system and the application of the Petri net method will be demonstrated.     

ADOL: a novel framework for automatic domain ontology learning

Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational Ontology to describe systematic knowledge of different subjects automatically. Currently, most of the ontologies are developed and extended manually, which requires the developers to possess certain professional knowledge and is time-consuming. In this paper, a framework to construct and extend Educational Ontology automatically is proposed.2 The proposed ontology learning framework, called ‘ADOL,’ can convert domain textbooks into a corresponding ontology automatically and efficiently. A case study on High School Physics shows that our approach is feasible and efficient.

     

Belief updating in a fuzzy expert system

We argue that with the addition of a rule-ranking procedure, our fuzzy expert system FLOPS can correct previous conclusions given new, possibly conflicting, information.     

An expert system for manufacturing systems machine selection

This work presents the development of a prototype expert system (ES) for the machine selection of manufacturing systems. This tool, called ESMRS (Expert System for Manufacturing Resource Selection) is used in a simulation based approach in order to structure the solution search mechanism and to overcome the try and error aspect. In fact, in such an approach a number of ‘simulation—ES optimization’ cycles are run until obtaining non-improvable performance measures. The ES main role is to suggest resource modifications based on due date related performance measures obtained through simulation. So, this paper introduces the ‘ES—simulation approach’ that constitutes the utilization scope of ESMRS and then describes the ES static and dynamic knowledge representation before presenting the basic ES features as well as its development using a commercial ES shell. Finally a simple case study illustrates the validity of the approach and its potential applicability for real cases.     

Xrays: A fuzzy expert system for qualitative XRD analysis

This paper describes the development of a fuzzy expert system termed XRAYS for identification of minerals via X-ray diffractograms. The system emulates the well-known (manual) Hanawalt method, thus avoiding the black-box approach of most computer search/match programs. The mineral subfile of the JCPDS Powder Diffraction file is stored in a database, from which the Hanawalt groups are created by the program. The expert system then carries out “manual” search following the steps prescribed for the Hanawalt method. Fuzzy comparisons and fuzzy arithmetic operations are employed in searching for matches. A list of candidate minerals is output in decreasing order of confidence. Graphical comparisons between the unknown pattern and candidate patterns are displayed on the screen to allow the diffractionists to make visual comparison as to the degree of match. Several examples containing from two to six minerals are used for illustration.     

A fuzzy expert system for aviation risk assessment

The Flight Operations Risk Assessment System (FORAS) is a risk modeling methodology which represents risk factors and their interrelationships as a fuzzy expert system. A FORAS risk model provides a quantitative relative risk index representing an estimate of the cumulative effects of potential hazards on a single flight operation. FORAS systematizes the process of eliciting human expertise, provides for a natural representation of the knowledge in an expert system, and automates the process of risk assessment. The FORAS tool is valuable to airline safety departments for examining risk trends, to pilots and dispatchers for assessing risks associated with each flight, and to airline management for quantifying the effects of making safety-related changes. The quantitative relative risk index generated by FORAS allows comparisons between flights, and facilitates the communication of safety issues throughout the organization.     

A fuzzy expert system for noise-induced sleep disturbance

In this paper, an attempt has been made to develop a fuzzy expert system for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modelling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. It has been established on the basis of findings of various researchers that the effect of noise on sleep disturbance depends to a large extent on age. The middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, we have compared our model results with some of the findings of researchers reported in International Journals.     

Support vector learning mechanism for fuzzy rule-based modeling: a new approach

This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.