Fuzzy Logic-based Model Research Articles

Coupling a mathematical and a fuzzy logic-based model for prediction of zinc ions separation from wastewater using electrodialysis

Abstract This paper presents experimental data, a fuzzy logic (FL) model, a mathematical model (MM) and a coupled MM–FL model for a laboratory scale electrodialysis (ED) cell. The aim was to predict separation percent (SP) of zinc ions as a function of concentration, temperature, flow rate and voltage. At first, a Sugeno type FL inference system was applied to model zinc ions separation from wastewater using ED. FL modeling results showed that there is an excellent agreement between the experimental data and the predicted values, with mean squared relative error (MSRE) of less than 0.01. Then, the results of a previously developed MM were presented. The MM related SP to hydrodynamic dimension of the ED cell and operation conditions via two distinct parameters. This ability favored the MM for scale-up applications. However, based on MSRE of the MM (about 24), it could not obviously predict the experimental data as well as FL. Hence, as a final step, the MM was coupled with FL to achieve benefits of both. It was found out that the developed coupled model (MM–FL) is able to predict SP of zinc ions at all operating condition and almost every dimension to a high degree of accuracy (MSRE = 0.05).

A fuzzy logic-based model for noise control at industrial workplaces

Ergonomics is a broad science encompassing the wide variety of working conditions that can affect worker comfort and health, including factors such as lighting, noise, temperature, vibration, workstation design, tool design, machine design, etc. This paper describes noise–human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model. These rules were determined by taking into consideration formal standards, experiences of specialists and the measurements patterns. The results of the model were compared with various statistics (correlation coefficients, max–min, standard deviation, average and coefficient of skewness) and error modes (root mean square error and relative error). The correlation coefficients were significantly high, error modes were quite low and the other statistics were very close to the data. This statement indicates the validity of the model. Therefore, the model can be used for noise control in any workplace and helpful to the designer in planning stage of a workplace.

Professional learning: A fuzzy logic-based modelling approach

Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of representative data of such variables (drawn from an in-service training programme in Greece) that were confronted to it. Through this research strategy we found that the proposed modelling managed to reveal statistically significant different trends in professional learning in concordance with an earlier qualitative approach, yet, quantitatively. Moreover, it highlights the advantages of the fuzzy logic approach towards capturing the information that is inherent in real, complicated data and gives rise to further heuristic ideas and research directions concerning professional learning.

A study towards fuzzy logic-based assessment of nasal harmony of rhinoplasty patients

A fuzzy logic-based model is used for generating an index which reflects the harmony of nasal proportions of a rhinoplasty patient. The index generated by the model is intended to be used as an objective tool for evaluating the harmony of nasal proportions of the patient. One of the possible uses of the index is to rate the nasal proportions of the patient relative to an ideal nose. Another possible use of the index is to generate a score for the patient before and after the rhinoplasty operation so that the improvement due to rhinoplasty operation can be determined objectively.

Adaptive clustering algorithm for recycling cell formation: An Application of fuzzy ART neural networks

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzy ART neural network is suggested. Especially, the modified Fuzzy ART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.

A fuzzy logic-based model for the multistage high-pressure inactivation of Lactococcus lactis ssp. cremoris MG 1363

The high-pressure inactivation (200 to 600 MPa) of Lactococcus lactis ssp. cremoris MG 1363 suspended in milk buffer was investigated with both experimental and theoretical methods. The inactivation kinetics were characterised by the determination of the viable cell counts, cell counts of undamaged cells, LmrP activity, membrane integrity, and metabolic activity. Pressures between 200 and 600 MPa were applied, and pressure holding times were varied between 0 and 120 min. Experiments were carried out in milk buffer at pH values ranging between 4.0 and 6.5, and the effect of the addition of molar concentrations of NaCl and sucrose was furthermore determined. The inactivation curves of L. lactis, as characterised by viable cell counts, exhibited typical sigmoid asymmetric shapes. Generally, inactivation of the membrane transport system LmrP was the most sensitive indicator of pressure-induced sublethal injury. Furthermore, the metabolic activity was inactivated concomitant with or prior to the loss of viability. Membrane integrity was lost concomitant with or later than cell death. For example, treatments at 200 MPa for 60 min in milk buffer did not inactivate L. lactis, but fully inactivated LmrP activity and reduced the metabolic activity by 50%. The membrane integrity was unaffected. Thus, the assay systems chosen are suitable to dissect the multistep high-pressure inactivation of L. lactis ssp. cremoris MG 1363. A fuzzy logic model accounting for the specific knowledge on the multistep pressure inactivation and allowing the prediction of the quantities of sublethally damaged cells was formulated. Furthermore, the fuzzy model could be used to accurately predict pressure inactivation of L. lactis using conditions not taken into account in model generation. It consists of 160 rules accounting for several dependent and independent variables. The rules were generated automatically with fuzzy clustering methods and rule-oriented statistical analysis. The set is open for the integration of further knowledge-based rules. A very good overall agreement between measured and predicted values was obtained. Single, deviating results have been identified and can be explained to be measurement errors or model intrinsic deficiencies.

Integrated fuzzy logic-based model for product modularization during concept development phase

Modular design is considered as a viable approach to retain the market share by offering large product variety in the minimum time frame. This paper presents a formal and integrated method for optimizing the performance attributes of prospective modules while modularizing the product architecture early in the concept development phase. A fuzzy logic-based approach is presented to handle the vague, imprecise and uncertain information available regarding a product at conceptual stage. Although the paper illustrates the procedure for minimizing the cost of modular architecture, in actual case, the methodology can also be used for optimization of other attributes such as quality, reliability, manufacturability, etc. A case example is presented to demonstrate the proposed methodology.

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent “imitate” teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments.

Development of a fuzzy logic-based rainfall-runoff model

Rainfall-runoff models are used to describe the hydrological behaviour of a river catchment. Many different models exist to simulate the physical processes of the relationship between precipitation and runoff. Some of them are based on simple and easy-to-handle concepts, others on highly sophisticated physical and mathematical approaches that require extreme effort in data input and handling. Recently, mathematical methods using linguistic variables, rather than conventional numerical variables applied extensively in other disciplines, are encroaching in hydrological studies. Among these is the application of a fuzzy rule-based modelling. In this paper an attempt was made to develop fuzzy rule-based routines to simulate the different processes involved in the generation of runoff from precipitation. These routines were implemented within a conceptual, modular, and semi-distributed model-the HBV model. The investigation involved determining which modules of this model could be replaced by the new approach and the necessary input data were identified. A fuzzy rule-based routine was then developed for each of the modules selected, and application and validation of the model was done on a rainfall-runoff analysis of the Neckar River catchment, in southwest Germany.

ON GROUP DECISION MAKING UNDER LINGUISTIC ASSESSMENTS

In this paper we consider a fuzzy logic-based model in group decision making, with a focus on the set of all alternatives and on the individual lingustic preference relations. Some choice processes are devoted to the model using consensus measures and linguistic ordered weighted averaging (LOWA) operator. A multiple criteria group decision model in linguistic setting and some aggregation processes are also considered. The model and the new processes allow to incorporate human consistency in decision support systems.

Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives

Switched reluctance motor drives may be used in many commercial applications due to their simplicity and low cost. These drives require rotor position feedback to operate. However, in many systems, rotor position sensors have disadvantages. In this paper, a position sensorless scheme is described which uses fuzzy modeling, estimation and prediction. An important feature is that saturation and real-time nonideal effects are not ignored, but that no mathematical model is required. Instead, a fuzzy logic-based model is constructed from both static and real-time motor data, and from this model the rotor position is estimated. The system also incorporates fuzzy logic-based methods to provide a high robustness against noise. This includes a fuzzy predictive filter which combines both fuzzy logic-based time-series prediction, as well as a heuristic knowledge-based algorithm to detect and discard feedback signal error. In addition, the method uses heuristic knowledge to choose the most desirable phase for angle estimation in order to minimize the effect of feedback error. It is also shown that, by using fuzzy logic, the estimation scheme offers a high robustness and reliability and is thus well suited to a wide range of systems.