Fuzzy System Model Research Articles

A New Perspective for Computational Social Systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT”, these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Assessing the sustainability of groundwater quality for irrigation purposes using a fuzzy logic approach

Deterioration of water quality poses significant threats to various aspects of life, particularly affecting agricultural irrigation, the primary source of food production for human consumption. In Iran, unregulated groundwater extraction for agriculture has resulted in a decline in groundwater quality, exemplified by a reduction in permitted wells for water exploitation in the Houmand-Absard aquifer from 47 to 20 over 18 years. Current water quality assessments for agricultural use often employ indicators focusing on specific ions, neglecting a comprehensive evaluation. This study introduces the Fuzzy Groundwater Quality Index (FGWQI), utilizing a fuzzy inference system model to appraise groundwater quality in the Houmand-Absard aquifer, specifically for irrigation in Iran. The FGWQI amalgamates five agriculture-oriented quality indicators: Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard Ratio (MHR), Kelly's Index (KI), and Potential Salinity (PS). Comparative analyses between FGWQI and the widely used MHR were conducted on water samples collected from twenty stations during the 2021 seasons. Laboratory analyses of the samples determined parameters including Sodium, Calcium, Magnesium, Chloride, and Sulfate. The FGWQI model outperformed traditional indicators, notably MHR, demonstrating more realistic assessments. Despite MHR deeming water quality unsuitable in 47% of cases where FGWQI surpassed 60, FGWQI indicated permissible results (30–70), suggesting that, with proper groundwater management, a greater water resource allocation to agriculture is feasible. By adopting FGWQI over MHR, water wells deemed suitable for irrigation won't be decommissioned, offering farmers increased flexibility in groundwater utilization in the case study region.

Fuzzy Inference System Development for Turbogenerator Failure Diagnosis on Floating Production Offloading and Storage Platform

This paper introduces a novel approach for diagnosing failures within a turbogenerator mineral lube oil system, employing a fuzzy inference system (FIS) model. The study leverages real operational data collected from supervisory monitoring sensors across four turbogenerators over a three-year operational span, resulting in a dataset comprising 40,456,663 input parameters. The failure modes were established through expert knowledge, using the Failure Mode, Effect, and Criticality Analysis (FMECA) documentation as the basis. Initially, the model’s universe variables were constructed using the sensor calibration range, and then the fuzzy membership functions were formulated based on the operational thresholds inherent to each measured parameter. The fault identification mechanism is underpinned by an inference system employing predefined rules, extrapolated from expert judgments encapsulating failure typologies specific to the turbogenerators’ mineral lube oil system, as delineated in the FMECA. The FIS model demonstrates notable efficacy in failure diagnosis with an overall performance evaluation of the system yielding satisfactory outcomes, having a 98.35% true positive rate for failure classification, coupled with a 99.99% true negative rate for accurate classification during normal system operation. These results highlight the visibility of the FIS model in diagnosing failures within the turbogenerator mineral lube oil system, thereby showcasing its potential for enhancing operational reliability and maintenance efficiency.

Comparison of Artificial Neural Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) Model in Predicting the Outlet Flow Rate of Passive Treatment System Column

Acid mine drainage (AMD) is one of the major environmental problems the mining and mineral processing industries face. Treatment of AMD involves active and passive treatment. In the long term, passive treatment is the most effective way to treat acid mine drainage, but it can be expensive. if handled properly. Therefore, the study of flow rate in a passive treatment system is one of the important ways to identify optimum hydraulic retention time to ensure the maximum percentage of heavy metal removal can be achieved while keeping the cost to a minimum level. This study focused on developing and comparing the Response Surface Methodology (RSM) model and Artificial Neural Fuzzy Inference System (ANFIS) model to predict the outlet flow rate of the passive treatment system column based on three parameters inlet flow time, thickness of peat soil bed, and inlet flow rate. The RSM model was created by Design-Expert software whereas MATLAB created the ANFIS model with 80% of data used for the model training and 20% of the data for model testing. The models' performances were compared in terms of statistical errors (AAPE, RMSE, R2, STD, minimum error, and maximum error). It was found the ANFIS model has performed better in predicting the outlet flowrate with R2 value of 0.99 RSM model with the value of 0.97. The inlet flow rate was an insignificant parameter affecting the outlet flow rate of the passive treatment column. From the 3-D surface response plot, the highest outlet flow rate is predicted to be 524 mL/min.

Algebraic disturbance estimation for a class of fractional order T-S fuzzy systems with noisy output measurements

This paper concerns with the design of algebraic disturbance estimation method for the fractional order Takagi-Sugeno (T-S) fuzzy systems with noisy output measurements. At first, the overall fractional order fuzzy system model is given for the studied system. By constructing a family of fractional order modulating functions, algebraic integral formulas are derived for the disturbance by using the overall fractional order fuzzy system model. Based on the system’s input and output, these formulas can be algebraically computed in both noise-free and noisy cases. Simulation results are shown to validate the efficiency of the proposed algebraic disturbance estimation method against output measurement noises.

Study on the Annual Runoff Forecast Model of the Main Stream of Nanxi River Based on PSO-ANFIS

In 2021, Wenzhou adopted measures to restrict the use of electricity, and the shortage of electricity became an important factor affecting the production and life of Wenzhou. Nanxi River is one of the main rivers in Wenzhou City, and its water resources are very rich. According to the statistics of the water conservancy planning of the Nanxi River basin, there are 96 hydropower stations in the Nanxi River basin, with a total installed capacity of 152100 kW, accounting for 57% of the installed capacity. The development and utilization of the Nanxi River water resources can alleviate the power shortage in Wenzhou power grid to a certain extent. The development and utilization of hydropower are closely related to the runoff of the basin. The river runoff is mainly determined by rainfall, underlying surface and upstream inflow. River runoff is affected by many factors in the process of formation, so it is difficult to improve its prediction accuracy. In order to improve the prediction accuracy of the runoff of the main stream of the Nanxi River, this paper introduces the runoff prediction model of particle swarm optimization adaptive fuzzy inference system (PSO-ANFIS). ANFIS model has the advantages of applying fuzzy rules and the nonlinear approximation ability of neural network, but the antecedent parameters of ANFIS model are prone to fall into local optimization. In order to improve the generalization ability of the antecedent parameters of ANFIS model, the PSO algorithm of global optimization is introduced to optimize the antecedent parameters of ANFIS. Through the application of the example, it is found that the decision coefficient of PSO-ANFIS model in the simulation stage is 0.987, and the decision coefficient in the prediction stage is 0.856. This model can be applied in the annual runoff forecast. Through comparison with ANFIS model, it is found that PSO-ANFIS model has better prediction effect.

Ten quick tips for fuzzy logic modeling of biomedical systems.

Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced researchers, who might overlook some important aspects or might make common mistakes. Malpractices and pitfalls, in turn, can lead to wrong or overoptimistic, inflated results, with negative consequences to the biomedical research community trying to comprehend a particular phenomenon, or even to patients suffering from the investigated disease. To avoid common mistakes, we present here a list of quick tips for fuzzy logic modeling any biomedical scenario: some guidelines which should be taken into account by any fuzzy logic practitioner, including experts. We believe our best practices can have a strong impact in the scientific community, allowing researchers who follow them to obtain better, more reliable results and outcomes in biomedical contexts.

Modeling of Fuzzy Systems Based on the Competitive Neural Network

This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.

Interval Type-3 T-S fuzzy system for nonlinear aerodynamic modeling

This paper presents an aircraft related application of interval type-3 (IT3) T-S fuzzy system for nonlinear aerodynamic modeling from recorded flight data. The theoretical base developed for design of IT3 T-S fuzzy system is utilized and longitudinal aerodynamic coefficients are modeled. These longitudinal aerodynamic coefficients are related with lift force, drag force and pitching moment. The flight data used for simulation belongs to ATTAS aircraft which is operating in quasi steady stall maneuver flight condition. The applicability of IT3 fuzzy system for modeling data uncertainties are demonstrated by adding the external noise in recorded data and making uncertainties associated with unvarying and missing data. The IT3 T-S fuzzy modeling method can suitably characterize the complex and nonlinear behavior of the aircraft in the presence of uncertainty and unsteadiness created due to aerodynamic / propulsive / structural interactions, separated air flow and mass properties changes etc. The performance comparison of generated IT3 T-S fuzzy aerodynamic model is also carried out with other state of the art modeling methods, the proposed fuzzy model shows good prediction capability in presence of various form of data uncertainties with associated noise. It is also noticed that as noise content in data increases, the percentage improvement in accuracy (in comparison with other fuzzy models) also appears. However, this improvement is based on nature of application and data. For clean data, the performance of IT3 fuzzy system is almost equivalent to other modeling methods, but with the increase of noise content and uncertainty in data, the performance improvement of IT3 fuzzy system enhances. Thus, performance of the IT3 fuzzy systems becomes more appealing when, uncertainty arises in the data in form of noise or missing/unvarying data points.

Sugeno fuzzy inference system modeling and DFT calculations for the treatment of pesticide-laden water by newly developed arginine functionalized magnetic Mn-based metal organic framework.

The uncontrolled utilization of pesticides poses a significant risk to the environment and human health, making its management essential. In this regard, a new arginine functionalized magnetic Mn-based metal-organic framework (Arg@m-Mn-MOF) was fabricated and assessed for the removal of cypermethrin (CYP) and chlorpyrifos (CHL) from aqueous system. The Arg@m-Mn-MOF was characterized by scanning electron microscopy, energy dispersive X-ray, Fourier transform infrared spectroscopy, X-ray diffraction, and Brunauer-Emmett-Teller analysis. Various parameters were optimized in a series of batch experiments and the following conditions were found optimal: pH: 4 and 5, contact time: 20min, adsorbent dosage: 0.6 and 0.8g L-1 with initial concentration: 10mg L-1 and temperature: 298K for CYP and CHL, respectively. The composite attained a maximum removal capacity of 44.84 and 71.42mgg-1 for CYP and CHL, respectively. The elucidated data was strongly fitted to the pseudo-second-order model of kinetics (R2 > 0.98) and Langmuir isotherm (R2 > 0.98). Based upon 350 experimental datasets obtained from batch studies and interpolated data, the adsorption capacity of the adsorbent was elucidated with R2 > 0.97 (CHL) and > 0.91 (CYP). The adsorption energy (- 11.67kcalmol-1) calculated by Gaussian software suggests a good interaction between arginine and CHL through H-bonding. The present study's findings suggested the prepared Arg@m-Mn-MOF as a promising adsorbent for the efficient removal of pesticides from agriculture runoff.

An experimental investigation on kinetic analysis of thermal degradation of shape stable composite phase change materials and adaptive neuro fuzzy inference system modeling for predicting mass loss

An experimental investigation on kinetic analysis of thermal degradation of shape stable composite phase change materials and adaptive neuro fuzzy inference system modeling for predicting mass loss

Fuzzy modeling and characterization of mechanical and biological properties of a selective laser melting shape: A comprehensive study

The bone scaffold is a porous structure that possesses suitable biological and mechanical properties similar to those of natural bone, thereby providing a suitable substrate for bone growth. The scaffold plays a temporary role in the bone repair process and is composed of degradable material that gradually degrades over time to enable new bone growth and replacement. The scaffolds are implanted using bone morphogenetics of transplanted bone-protein (BMP-7) and implanted subcutaneously to assess biological properties and tissue growth. This study demonstrates the ability to design and fabricate a titanium scaffold with a porous architecture using the Selective Laser Melting (SLM) method, employing two different geometries. The scaffolds' mechanical properties are assessed via a compression test, and the results are utilized for finite element analysis (FEA), predicting the designed metallic scaffold's optimal geometric shape and porosity percentage. The biological response of the scaffolds is evaluated via simulated body fluid (SBF) and phosphate buffer saline (PBS) analyses. The findings indicate that the 8-sided scaffold has superior mechanical and biological features compared to the circular and 6-sided geometries. To predict the scaffold's specifications before conducting the experiment, a fuzzy modeling system is used to model the effect of inputs such as weight percentage, elastic modulus, porosity, and Poisson ratio on the compressive strength output variable.

DOMAIN-ADAPTIVE TSK FUZZY SYSTEM BASED ON MULTISOURCE DATA FUSION FOR EPILEPTIC EEG SIGNAL CLASSIFICATION

In recent years, machine learning methods based on epileptic signals have shown good results with brain-computer interfaces (BCIs). With the continuous expansion of their applications, the demand for labeled epileptic signals is increasing. For a large number of data-driven models, such signals are not suitable, as they extend the calibration cycle. Therefore, a new domain-adaptive TSK fuzzy system model based on multisource data fusion (DA-TSK) is proposed. The purpose of DA-TSK is to maintain high classification performance when the amount of labeled data is insufficient. The DA-TSK model not only has a strong learning ability to learn characteristic information from EEG data but is also interpretable, which aids in the understanding of the analytic process of the model for medical purposes. In particular, this model can make full use of a small amount of labeled EEG data in the source domain and target domain through domain adaptation. Therefore, the DA-TSK model can reduce data dependence to a certain extent and improve the generalization performance of the target classifier. Experiments are performed to evaluate the effectiveness of the DA-TSK model on public EEG datasets based on epileptic signals. The DA-TSK model can obtain satisfactory accuracy when the labeled data are insufficient in the target domain.

Sampled-data output tracking control based on T–S fuzzy model for cancer-tumor-immune systems

This study investigates the tracking control issue for a cancer-tumor immune nonlinear model, which explains the interaction between cancer cells and the immune system in the body. To achieve this, the nonlinear model is first converted into linear sub-models using the Takagi–Sugeno (T–S) fuzzy methodology. The tracking and control of cancer cell proliferation while preserving immune cells is proposed using a sample-data output tracking control technique. Time-dependent discontinuous Lyapunov Krasovskii functional is recommended based on the improved Wirtinger inequality, this may provide additional information about the sawtooth structure of the sampling interval. Less conservative stability criteria are extracted having the form of linear matrix inequalities (LMIs) in consequence of extended reciprocally convex matrix inequality and Wirtinger-based integral inequality. The proposed approach achieves the stabilization of an augmented time delay system that connects with a given fuzzy nonlinear model and tracking error system. Furthermore, the proposed approach reduces the impact of external disturbances under the H∞ norm bound.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

FUZZY LOGIC CONTROL (FLC) FOR A YARN CONDITIONING SYSTEM

Conditioning machines have a wide use in the textile industry. For this reason, it is important to control the conditioning machines precisely. Fuzzy logic control has found successful application areas in the control of electrical machines. In this study, fuzzy logic control was used to control the conditioning system. In the computer aided conditioning experiment setup developed for this purpose, information is received via sensors, the measurement values are directly transferred to the computer, and the control of the system is done via the computer. Different experimental measurements were used to evaluate the reliability of FLC. In this study, 100% cotton selected in the most used number range in the market. Yarns in a vacuum environment with saturated steam at different pressures and temperatures. Measurements were taken one hour after conditioning 1 day then repeat after 1 week, after 2 weeks, and after 3 week. A fuzzy model of the yarn conditioning system was created using experimental data. Control of the conditioning machines with fuzzy logic controller (controller) has been done successfully. It has been observed that the fuzzy logic controller works very well, catches the desired set value in a short time and keeps the system at this value.

Deformation rate of engineered wood flooring with response surface methodology and adaptive network-based fuzzy inference system.

Controlling the deformation rate is the key to improving the product quality of engineered wood flooring. In this work, the changes in the deformation rate of engineered wood flooring were in focus with cold-pressing, response surface methodology, and adaptive network-based fuzzy inference system were used to explore the relationship between deformation rate and processing parameters, including adhesive spreading rate, pressing time, and pressing pressure. According to the results, the deformation rate was positively related to pressing time, while it increased first and then decreased with both the increase of adhesive spreading rate and pressing pressure. Meanwhile, a mathematical model was developed, and the significant influence of each term on the deformation rate was analyzed. This model had high feasibility and can be used to describe the relationship between the deformation rate and processing parameters. Furthermore, an adaptive network-based fuzzy inference system model was established. It has higher accuracy than that of the response surface methodology model, and it can be used for predicting deformation rate and optimizing processing parameters. Finally, an optimal processing conditions with the lowest deformation rate was determined as follows: 147 g/m2 adhesive spreading rate, 12s pressing time, and 1.2 MPa pressing pressure, and it hope to be adopted in the industrial processing of engineered wood flooring with respective of the higher product quality and lower production costs.

Topology structure optimization of evolutionary hierarchical fuzzy systems

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems.

Fuzzy inference system model for the spectrum characteristic periods of the Türkiye Building Earthquake Code (2007)

Seismic design codes define response spectra with crisp numerical classifications of seismic parameters, which mainly affect the spectrum's shape and determination of seismic design loads. The efficiency of structural safety and construction costs depends on the optimum design and accurately determined seismic forces. As presented in the seismic design codes, several parameters are utilized to calculate the seismic design forces with response spectra. This study proposes a rule-based fuzzy inference system (FIS) model with fuzzy set numbers to determine the relevant parameters. By defining the soil profile thickness and shear wave velocity as inputs, the model generates the spectrum characteristic periods specified in the Türkiye Building Earthquake Code (TBEC 2007). The response spectra of twenty different samples with the FIS and crisp models were generated and compared to assess the model's superiority. Unlike crisp seismic code classifications, the proposed FIS model accounts for imperfections in soil group selection and topmost soil layer thickness, offering a more realistic representation of uncertainties and proving to be an effective tool for addressing linguistic vagueness in seismic response spectra analysis. The comparison between fuzzy and crisp output seismic parameters revealed significant differences in response spectra shape and spectrum intensity values. The FIS model-generated spectra were more conservative in certain building locations, while in others, they provided similar or lower values, suggesting potential cost savings in design. The FIS model demonstrates its efficacy in producing more accurate and robust designs by considering the uncertainties inherent in the problem. Furthermore, this approach has the potential to be extended to study seismic parameters of other design codes, although further research is required to comprehensively explore its capabilities and limitations.

Fuzzy inference system model for the spectrum site coefficients of the Türkiye Building Earthquake Code (2018)

The response spectra in the seismic design codes guide the seismic design force calculations that are crucial for structural safety and construction costs. In general, they are determined by crisp logic-based numerical classifications of seismic parameters, which primarily affect the shape and values of the spectrum. Crisp logic response spectra generation may have limitations in the accurate determination of relevant parameters. In this paper, a fuzzy inference system (FIS) rule-based model is proposed to address this issue. The proposed model uses spectral acceleration coefficients and shear wave velocity as inputs to generate the site coefficients of the response spectra according to the Türkiye Building Earthquake Code (TBEC 2018). The FIS model spectra generation is compared with the crisp logic model for thirty-five examples, and the model performance is evaluated. The study demonstrates significant differences between the two models in terms of the response spectra' shape and acceleration spectrum intensity values, but the FIS model provides comparatively better accuracy in response spectra generation. The proposed model can be modified and applied to various parameters for generating response spectra in different seismic design codes with the exposition of fuzzy logic rules that show the concerned problem's internal working mechanism.