Fuzzy Knowledge Modeling Research Articles

Skill‐based education through fuzzy knowledge modeling for e‐learning

AbstractNowadays outcome‐based methods are adapted in e‐learning system to meet the need of the novel development of e‐learning systems for improved web‐based retrieval results. Typically, knowledge retrieval process is denoted by the production rule, frame, semantic network, fuzzy logic, predicate logic, and group of skill concept. To get the optimized result in proposed skill‐based e‐learning, the fuzzy knowledge model is applied. In knowledge retrieval, the fuzzy membership value of the knowledge and the combinations of framed rules are used to acquire the knowledge. The fuzzy techniques are adapted for analyzing the retrieved knowledge concept of individual skills like Inadequate (Ia), Adequate (A), Value added adequate (Vaa), and Integrated skill (I) and in fuzzy inference system in skill‐based education provides a decision about the learner community skill set and it promotes the excellence skill through the delivery of the suitable courses to the learners instead of supplying common courses to different skilled persons. In the existing knowledge modeling methods known as the Knowledge Capturing and Modeling, concept map‐based knowledge modeling confirm the learners to have known or unknown domain concept. Further, many of the researchers present on the analysis of the learner performances, behavior, learning environment, etc. The proposed paper investigate the individual skill abilities and it is suggested a set of courses in adaptive curriculum and syllabus to the learner and also it adapts andragogy in skill‐based education, to model the fuzzy knowledge, the fuzzy membership function and rules are used.

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower ( P < 0.05) and the values of PTABC and DC of BIS target were significantly higher ( P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences ( P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher ( P < 0.05) than in the manual control group. In contrast, there were no significant differences ( P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.

Derived fuzzy knowledge model for estimating the depth of anesthesia.

Reliable and noninvasive monitoring of the depth of anesthesia (DOA) is highly desirable. Based on adaptive network-based fuzzy inference system (ANFIS) modeling, a derived fuzzy knowledge model is proposed for quantitatively estimating the DOA and validate it by 30 experiments using 15 dogs undergoing anesthesia with three different anesthetic regimens (propofol, isoflurane, and halothane). By eliciting fuzzy if-then rules, the model provides a way to address the DOA estimation problem by using electroencephalogram-derived parameters. The parameters include two new measures (complexity and regularity) extracted by nonlinear quantitative analyses, as well as spectral entropy. The model demonstrates good performance in discriminating awake and asleep states for three common anesthetic regimens (accuracy 90.3 % for propofol, 92.7 % for isoflurane, and 89.1% for halothane), real-time feasibility, and generalization ability (accuracy 85.9% across the three regimens). The proposed fuzzy knowledge model is a promising candidate as an effective tool for continuous assessment of the DOA.

A synthesis of fuzzy rule-based system verification

The verification of fuzzy rule bases for anomalies has received increasing attention these last few years. Many different approaches have been suggested and many are still under investigation. In this paper, we give a synthesis of methods proposed in literature that try to extend the verification of classical rule bases to the case of fuzzy knowledge modeling, without needing a set of representative input. Within this area of fuzzy validation and verification (V&V) we identify two dual lines of thought leading to what is identified as static and dynamic anomaly detection methods. Static anomaly detection essentially tries to use similarity, affinity or matching measures to identify anomalies within a fuzzy rule base. It is assumed that the detection methods can be the same as those used in a non-fuzzy environment, except that the former measures indicate the degree of matching of two fuzzy expressions. Dynamic anomaly detection starts from the basic idea that any anomaly within a knowledge representation formalism, i.e. fuzzy if–then rules, can be identified by performing a dynamic analysis of the knowledge system, even without providing special input to the system. By imposing a constraint on the results of inference for an anomaly not to occur, one creates definitions of the anomalies that can only be verified if the inference process, and thereby the fuzzy inference operator is involved in the analysis. The major outcome of the confrontation between both approaches is that their results, stated in terms of necessary and/or sufficient conditions for anomaly detection within a particular situation, are difficult to reconcile. The duality between approaches seems to have translated into a duality in results. This article addresses precisely this issue by presenting a theoretical framework which enables us to effectively evaluate the results of both static and dynamic verification theories.