Sugeno-type Fuzzy Inference System Research Articles

Predictive Modelling and Optimization of the Mechanical Properties of Laser-Coated NB/SiC/Ni Welds Using an ANFIS

In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties were taken as the output of the ANFIS. The microstructure of welds was analysed using scanning electron microscopy with an energy-dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi’s design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction

We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we considered several input features, including precipitation, temperature, evaporation, relative humidity, soil type, and GWL Lag. A feature importance analysis using regression tree ensemble learning reveals GWL lag as the most relevant feature and soil type as the least relevant. We eliminate features with low importance scores (soil type, temperature, and evaporation) to improve the computational efficiency. Our approach leverages Fuzzy C-Means (FCM) clustering to develop a Sugeno-type FIS with definite clusters. The dataset is clustered based on feature similarity, and Gaussian membership functions are assigned to each cluster. The mapping of each point is controlled by the range and standard deviation of the Gaussian membership functions. We train the model keeping 70% of the dataset, iteratively tuning the parameters of the Gaussian membership function for all clusters through the Invasive Weed Optimization (IWO) algorithm. The performance of the model is then evaluated using the remaining 30% of the datasets. The F-IWO-GWL model accurately predicts GWL fluctuations, achieving a high correlation coefficient (R = 0.89), low normalized root mean square error (nRMSE = 0.18), and minimal bias (bias = 0.08). A comparative analysis involving seventeen benchmark algorithms reveals the superior performance of our algorithm. To ensure a fair comparison, we calculated key metrics including Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc). The F-IWO-GWL algorithm exhibits the lowest values for AIC, BIC, and AICc among all tested algorithms, suggesting the best goodness-of-fit. This study provides a robust approach for predicting GWL fluctuation, applicable to various groundwater management scenarios. It offers valuable insights for policymakers and stakeholders involved in groundwater management, aiding informed decision-making.

Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes

The removal of turbidity from abattoir wastewater (AWW) by electrocoagulation (EC) was modeled and optimized using Artificial Intelligence (AI) algorithms. Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization (PSO), and genetic algorithms (GA) were the AI tools employed. Five input variables were considered: pH, current intensity, electrolysis time, settling time, and temperature. The ANN model was evaluated using the Levenberg-Marquardt (trainlm) algorithm, while the ANFIS modeling was accomplished using the Sugeno-type FIS. The ANN and ANFIS models demonstrated linear adequacy with the experimental data, with an R2 value of 0.9993 in both cases. The corresponding statistical error indices were RMSE (ANN = 5.65685E-05; ANFIS = 2.82843E-05), SSE (ANN = 1.60E-07; ANFIS = 3.4E-08), and MSE (ANN = 3.2E-09; ANFIS = 8E-10). The error indices revealed that the ANFIS model had the least performance error and is considered the most reliable of the two. The process optimization performed with GA and PSO considered turbidity removal efficiency, energy requirement, and electrode material loss. An optimal turbidity removal efficiency of 99.39 % was predicted at pH (3.1), current intensity (2 A), electrolysis time (20 min), settling time (50 min), and operating temperature (50 °C). This represents a potential for the delivery of cleaner water without the use of chemicals. The estimated power consumption and the theoretical mass of the aluminium electrode dissolved at the optimum condition were 293.33 kW h/m3 and 0.2237 g, respectively. The work successfully affirmed the effectiveness of the EC process in the removal of finely divided suspended particles from AWW and demonstrated the suitability of the AI algorithms in the modeling and optimization of the process.

A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm

This paper presents a novel approach for individual design perception modeling using the YUKI algorithm-trained Fuzzy Inference System. The study focuses on understanding how individuals perceive design based on personality traits, particularly openness to experience, using the YUKI algorithm and Fuzzy C-means clustering algorithm. The approach generates several Sugeno-type Fuzzy Inference System models to predict design perception, to minimize the Root Mean Squared Error between the model prediction and the actual design perception of participants. The results demonstrate that the suggested method offers more accurate predictions compared to the traditional Fuzzy C-means Fuzzy Inference System and Deep Artificial Neural Networks, and the Root Mean Square deviation for individual design perceptions falls within a satisfactory range of 0.84 to 1.32. The YUKI algorithm-trained Fuzzy Inference System proves effective in clustering individuals based on their level of openness, providing insights into how personality traits influence design perception.

Assessing groundwater vulnerability potential using modified DRASTIC in Ajabshir Plain, NW of Iran.

The vulnerability of groundwater as the primary source of water for human survival should be assessed for the purpose of pollution management. The Ajabshir plain, one of the major agricultural areas in the northwest of Iran, is always prone to pollution. Therefore, to prevent the increase in pollution, it is necessary to determine the polluting factors and areas prone to groundwater pollution. In this study, by modifying the DRASTIC method using the land-use layer, called DRASTICL, vulnerable areas and pollution index were mapped. To ensure dealing with the uncertainty of the parameters, the DRASTICL model was optimized utilizing the Sugeno-type fuzzy inference system. The models were validated based on nitrate pollution. The correlation of DRASTICL and its optimized model with the nitrate pollution are 0.32 and 0.8, respectively. The results of this study show that integrating the DRASTIC model and fuzzy knowledge is an instrumental way for assessment of vulnerability potential.

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite "fuzzy" ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. FST scores, and p-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1-50 k SNPs ranged from -0.4-11.7 and -0.3-3.8% higher than FST and p-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both FST scores and p-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.

Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems

This research work investigates the mechanical and wear behaviour of graphene (C) and zirconium di-oxide (ZrO2) reinforced Aluminium alloy 6061 hybrid nano composites (AMMHNCs) fabricated by ultrasonic-assisted stir casting method. Graphene and ZrO2 are selected as reinforcements for increasing the wear resistance and hardness of the base alloy AA6061. The mixing proportions of graphene and ZrO2 reinforced with AA6061 in weight are 100% AA6061/0% Graphene/0% ZrO2, 98.5% AA6061/0.5% Graphene/1% ZrO2, 97.5% AA6061/0.5% Graphene/2% ZrO2, 98% AA6061/1% Graphene/1% ZrO2, 97% AA6061/1% Graphene/2% ZrO2. Microstructural study was carried out using optical and scanning electron microscopic images to analyse the dispersion of reinforcements in the composite. The results shown that, ultrasonic-assisted stir casting method improves the uniformity in dispersion of reinforcements. The hardness, tensile, impact and wear test were carried out based on ASTM standards to analyse the properties in the proposed composite specimens. It was observed that, the hardness, tensile strength and impact strength are increases by 21.88%, 69.42% and 78.57% respectively and percentage elongation is decreased by 63.52% with the increase of reinforcements. Wear resistance increases with the increase of reinforcements. In order to analyse the wear behaviour originality of new composite under wear test parameters, Artificial Neural Network (ANN) and Artificial Neuro Fuzzy Inference Systems (ANFIS) models were used to predict the wear rate for experimented and non-experimented parameters. The prediction analysis was useful in studying the wear behaviour of the composite. Comparative analysis for ANN and ANFIS was performed and the results shown that, ANFIS model predicted with accuracy of R2 with 99.9%.

Neuro-fuzzy network incorporating multiple lexicons for social sentiment analysis.

We have proposed MultiLexANFIS which is an adaptive neuro-fuzzy inference system (ANFIS) that incorporates inputs from multiple lexicons to perform sentiment analysis of social media posts. We classify tweets into two classes: neutral and non-neutral; the latter class includes both positive and negative polarity. This type of classification will be considered for applications that aim to test the neutrality of content posted by the users in social media platforms. In our proposed model, features are extracted by integrating natural language processing with fuzzy logic; hence, it is able to deal with the fuzziness of natural language in a very efficient and automatic manner. We have proposed a novel set of 64 rules for the proposed neuro-fuzzy network that can classify tweets correctly by working on fuzzy features fetched from VADER, AFINN and SentiWordNet lexicons. The proposed novel rules are domain independent, i.e., we can extend these rules for any textual data that employs lexicons. The antecedent and consequent parameters of the ANFIS are optimized by gradient descent and least squares estimate algorithms, respectively, in an iterative manner. The key contributions of this paper are: (1) a novel neuro-fuzzy system: MultiLexANFIS that takes as its input the positive and negative sentiment scores of tweets computed from multiple lexicons—VADER, AFINN and SentiWordNet, in order to classify the tweets into neutral and non-neutral content, (2) a novel set of 64 rules for the Sugeno-type fuzzy inference system—MultiLexANFIS, (3) single-lexicon-based ANFIS variants to classify tweets when multiple lexicons are not available and (4) comparison of MultiLexANFIS with different fuzzy, non-fuzzy and deep learning state of the art on various benchmark datasets revealing the superiority of our proposed neuro-fuzzy system for social sentiment analysis.

Fuzzy based relaying scheme for high impedance faults in DG integrated distribution system

Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions.

Speed Control of DC Motor Using Intelligent Controllers

DC Motors are broadly utilized in mechanical applications, home appliances and robot controllers on account of their high unwavering quality, adaptability and low cost, where speed and position control of motor are required. The activity of a DC motor is performed by conventional controllers and intelligent controllers in MATLAB environment. The speed control of a dc motor utilizing conventional controllers (PID, IMC) and intelligent controllers (FLC, ANFIS) in view of MATLAB simulation program. A numerical model of the process has been created utilizing genuine plant information and afterward conventional controllers and intelligent controllers has been planned. The outcome acquired as rise time, settling time. Out of these controllers FUZZY can give a superior outcome. Another intelligent controller like ANFIS Controller was created based on Sugeno type FIS along with PID can give a superior performance like quicker settling time, and its sensitivity to applied load. A relative investigation of execution assessment of all controllers has been finished.

A Method of Optimizing the Rule Base in the Sugeno Fuzzy Inference System Using Fuzzy Cluster Analysis

This paper develops a method of optimizing the rule base in the Sugeno-type fuzzy inference system using fuzzy cluster analysis for the design problems of control systems for complex technical objects. A hybrid control model with sequential interactions of classical and fuzzy controllers is used to validate the results. In the developed adaptive system of neuro-fuzzy inference, the rule base for the fuzzy controller is automatically formed based on the knowledge about the object obtained when it has been controlled by the classical controller, which excludes an expert's participation in the formation of the rule base. The optimization problem is solved using the method of grouping the values of the input and output signals in order to reduce the number of rules and to increase the speed of the control system of the technical object. The FCM clustering algorithm is used to group the values. As a result of the algorithm operation, not all of the set input actions arrive at the fuzzy controller input. Only cluster centers determined by fuzzy sets arrive, and the boundaries between clusters are also fuzzy. The effectiveness of the proposed grouping method for ensuring the effective control of a complex technical object under conditions of uncertainty is proved.

Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur (Curcuma angustifolia) starch

Chhana podo, a popular dairy product of India, is prepared by baking the dough of chhana (heat-acid coagulated milk solids), semolina and sugar. Heat induced moisture loss during baking influences the chain of physico-chemical changes, which in turn determine the quality of the final product. Thus, in this work, modelling approaches for predicting moisture transfer during baking of chhana podo prepared by replacing semolina with tikhur (Curcuma angustifolia) starch were developed. Moisture loss, moisture ratio, specific volume, oven spring, volumetric expansion and other physico-chemical quality changes of the dough were determined during baking, and were used in the development of moisture transfer models. The effective moisture diffusivity of chhana podo increased from 8.10 × 10–9 to 37.05 × 10–9 m2/s as baking temperature increased from 110 to 150 °C. The artificial neural networks (ANN) model based on multilayer feed-forward (MLFF) algorithm and radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) models based on Sugeno-type fuzzy inference system were developed to predict moisture ratio, and their predictive performance was compared with classical multiple linear regression and empirical models. Amongst the four prediction approaches, the MLFF neural network with baking temperature and time as input factors, and with 12 neurons in the hidden layer, produced the best performance in predicting moisture ratio with R2 as high as 0.9946 (99.46% accuracy) and RMSE of 0.0188 for the entire range of baking temperature studied. Sensitivity analysis showed that baking temperature was the most influential parameter (strength ratio was 0.89) deciding moisture transfer. MLFF neural network was observed to be a simple and efficient technique for predicting the complex moisture transfer phenomenon during baking, which could be used by the industry to optimize the baking conditions.

IOT BASED SMART AGRICULTURE MONITORING SYSTEM USING FUZZY LOGIC

India, Agriculture take place a essential role today. The Indian economic growth is also badly affected by Agriculture, as about 61.5% of total population is directly or indirectly is always depend upon the agricultural related activities. Dealing a various type of application in modern information technology era. This type of system activities will solve a series of problems faced by today farmers situation. The main aim of the project is to implement the automation in agricultural system, where it increases the efficiency in farming effective water irrigation, motion monitor around the field and fertility of soil. Corresponded data are collected from the sensor nodes and data are analyzed each session for irrigation scheduling using fuzzy logic in order to optimize the water usage. Comparing of those Mamdani-type FIS and Sugeno-type FIS was also done for water irrigation. The Fuzzy logic is implemented using NodeMCU micro controller. The captured image from the camera can be sent to mobile through ESP8266 Wi-Fi module. This system also used to check whether the plant is affected by any disease from its grain image using image processing techniques and tested using MATLAB.

Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank

The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Threfore, in this paper will use fuzzy inference systems to control of level 2 are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inference systems is evaluated. This paper summarizes the essential variation among the Mamdani-type and Sugeno-type fuzzy inference systems with setpoint of level is 10 centimeter. Matlab fuzzy logic toolbox is used for the simulation of both the models. This also confirms which one is a superior choice of the two fuzzy inference systems to control of level 2 in tank 2. The results show madani-type fuzzy inference system is superior as compared to sugeno-type fuzzy inference system.

Analysing wear behaviour of Al–CaCO3 composites using ANN and Sugeno-type fuzzy inference systems

Design of experiment for the development of stir cast calcium carbonate-reinforced aluminium composite is a search for optimum combination of material and process control parameters for best physical and mechanical properties. A soft-computing model can accurately learn the complex interactions between process parameters to provide great insights in the development of this composite. This paper demonstrates and analyses the potential of artificial neural network (ANN) and Sugeno-type fuzzy inference systems (FIS) for wear behaviour prediction of calcium carbonate-reinforced aluminium composites. The models were trained with data collected from the experiment. The data consist of filler particle size of 150 μm with weights fractions varied from 0 to 25 wt%, in step of 5. Wear test data at different time of contacts (30, 60, 90, 120 and 150 s) and variable loads of 2.27 N, 4.54 N and 6.80 N were collected, resulting to 120 length vectors. Comparing the experimental results of wear test with those predicted using the ANN and Sugeno-type FIS, the integration of calcium carbonate particulate enhanced the wear characteristics of Al matrix up to 200%. On the use of back-propagation neural network with 4–3–1 architecture for wear prediction, the Levenberg–Marquardt training algorithm performs better. For Sugeno-type FIS, the Gaussian membership function resulted to the best prediction of wear rate. When ANN and Sugeno-type FIS performance on the test set were analysed based on some statistical parameters, the later returned an R2 value of 0.9775 as against ANN’s value of 0.3684. The predicted wear rate using ANFIS with Gaussian membership functions was in good agreement with the experimental values.

Hybrid intelligent controller design for an unstable Electromagnetic Levitation System: A fuzzy interpolative controller approach

This article presents the design and implementation of hybrid intelligent controller for the dynamically nonlinear and unstable electromagnetic levitation system (EMLS). The hybrid design is having the intelligence of the fuzzy interpolative controller (FIC) with estimation ability and noise immunity achieved by means of the Kalman filter. The fuzzy inference system (FIS) is replaced by fuzzy linear interpolation networks based on look-up table to form the fuzzy rule base in ordered to reduce the computational complexity and to boost up the execution speed of the control approach as compared to conventional Mamdani or Sugeno type FIS toolkits. The proposed design stabilises the EMLS under wide initial and assorted operating conditions. Further, the proposed controller maintains the performance robustness under 0-25% of vertical payload disturbance by holding the steel ball within the safe limits. Simulation results are presented to validate the novelty and effectiveness of the proposed approach for EMLS.

Collation of Mamdani-Type and Sugeno-Type Fuzzy Inference System for Condition Monitoring of Power Transformer

Collation of Mamdani-Type and Sugeno-Type Fuzzy Inference System for Condition Monitoring of Power Transformer

ANN and ANFIS Approaches to Calculate the Heating and Cooling Degree Day Values: The Case of Provinces in Turkey

Energy analysis plays a vital role in developing an optimum and cost-effective design for the heating, ventilating and air conditioning system of a building. The values of heating degree days (HDD) and cooling degree days (CDD) are used to analyze the demand for energy for buildings. In this study, the HDD and CDD values for Turkey were calculated using estimated temperature values. An artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) were applied for the estimation of temperature values. The Levenberg–Marquardt training algorithm was used for the feedforward backpropagation of the ANN, whereas the Sugeno-type fuzzy inference system was used for the ANFIS. Meteorological data from the last 11 years were used for the proposed approaches, and these were applied to different stations (n = 50) of the Turkish State Meteorological Service. In relation to the insulated building design, base temperatures have been considered as 16 °C and 18 °C for the HDD and 22 °C and 24 °C for the CDD based on a literature review. Two novel maps were prepared for the CDD and HDD. The calculated degree days and the maps are consistent with those from the Turkish State Meteorological Service.

Asphaltene adsorption using green nanocomposites: Experimental study and adaptive neuro-fuzzy interference system modeling

Asphaltene deposition is a known problem that causes significant cost increases in the oil industry. Two bio-templated adsorbents, namely the NiO/ZSM-5 and NiO/AlPO-5 nanocomposites, were used as new green adsorbents to remove asphaltene from a model oil solution. Composite adsorbents were characterized by FTIR, BET, TEM and XRD analysis. Batch adsorption experiments were carried out as a function of D/C0 [(g)adsorbent/(mg/l)initial], pH, and temperature (K). Results showed that maximum adsorption is obtained at D/C0 = 0.072[g/(mg/l)] with a pH of 4.8 and a temperature of 298 K for NiO/ZSM-5 and D/C0 = 0.084[g/(mg/l)] with a pH of 3.4 and a temperature of 298 K for NiO/AlPO-5. In the experimental data, equilibrium adsorption models were introduced and their constants were calculated. The equilibrium adsorption data on NiO/ZSM-5 were well matched to the Freundlich model at 298 K and 325 K, and Temkin model at 342 K and 353 K. For the NiO/AlPO-5 adsorption data, the Temkin model was the best model showing strong adsorption interactions of asphaltene and adsorbent. The adaptive neuro-fuzzy interference system (ANFIS) was also used to model and predict the amount of asphaltene adsorbed by the proposed nanocomposites. ANFIS designed by triangular-shaped membership functions with three nodes and first-order polynomial Sugeno type FIS was the optimal structure and gave R2 = 0.9999 and R2 = 0.9996 for train and test data, respectively. Finally, Monte Carlo algorithm was used for sensitivity analysis on the input variables which is necessary for process optimization. Results demonstrated that D/C0, pH, and temperature have the highest effect on asphaltene removal by nanoparticles.

Identification of atrial fibrillation using descriptive statistic features and adaptive Neuro-Fuzzy inference system

Atrial fibrillation (AF) is one of the most common arrhythmia which can cause a serious problem. Nevertheless, well treated AF might not lead to any further complication. Early detection of AF could be an important preventative step that have to be conducted. In this article, we aim to make an automatic detection of atrial fibrillation. Seven descriptive statistic features have been utilized to detect AF. The features obtained could differ between two condition: normal and AF. Later, we use Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classification method. Sugeno-type fuzzy inference system along with Gaussian type of fuzzy are utilized to classify the condition. The proposed method is applied on MIT-BIH Atrial Fibrillation database. The performance obtained from this proposed method might be considered for a medical application.