Sugeno-type Fuzzy Inference System Research Articles

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.

THE IMPACT OF FUZZY LOGIC ON LOGISTICS OPTIMIZATION: A COMPREHENSIVE REVIEW

Fuzzy logic has emerged as a pivotal tool in the optimization of logistics processes, particularly for transportation routing, due to its capacity to handle uncertainties and nonlinear variables effectively. Developed by Lotfi Zadeh in the 1960s, fuzzy logic extends classical logic by allowing the modeling of imprecision and uncertainty, making it ideal for logistics contexts where variables like traffic conditions, delivery times, and customer demands can be highly variable. Unlike traditional models that require precise and constant variables, fuzzy logic operates with degrees of truth, integrating imprecise and subjective data. The application of fuzzy logic in transportation routing can lead to significant cost reductions by modeling variables as fuzzy functions. This approach accommodates variations in transit times and fuel costs and allows for dynamic route adjustments based on uncertain demand forecasts. Such flexibility enhances route optimization, reduces waste, and improves resource utilization. Additionally, fuzzy logic contributes to operational efficiency by managing nonlinear variables such as variable demand and weather conditions, enabling more adaptable and flexible route planning. Several studies underscore the efficacy of fuzzy logic in logistics. For instance, Lau et al. (2009) demonstrate the use of a fuzzy logic-guided genetic algorithm for optimizing vehicle routes considering both distance and time. Olenych and Gukaliuk (2017) present a Sugeno-type fuzzy inference system for transport network routing, while Olenych et al. (2018) extend this model to include additional variables such as traffic and road conditions. Huang et al. (2013) address the Fourth Party Logistics Routing Problem using fuzzy numbers, and Sheu (2007) introduces a fuzzy-based methodology for logistics distribution. Xiao and Hu (2017) optimize logistics for bulk commodities using fuzzy and neural network models, and Wang et al. (2014) propose a fuzzy clustering algorithm for customer grouping. These studies collectively highlight how fuzzy logic, combined with various optimization techniques, enhances decision-making and operational efficiency in logistics.

Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles

In this paper, an explainable intelligence model that gives the logic behind the decisions unmanned aerial vehicle (UAV) makes when it is on a predefined mission and chooses to deviate from its designated path is developed. The explainable model is on a visual platform in the format of if-then rules derived from the Sugeno-type fuzzy inference model. The model is tested using the data recorded from three different missions. In each mission, adverse weather, conditions and enemy locations are introduced at random locations along the path of the mission. There are two phases to the model development. In the first phase, the Mamdani fuzzy model is used to create rules to steer the UAV along the designated mission and the rules of engagement when it encounters weather and enemy locations along and near its chosen mission. The data are gathered as UAV traverses on each mission. In the second phase, the data gathered from these missions are used to create a reverse model using a Sugeno-type fuzzy inference system based on the subtractive clustering in the data. The model has seven inputs (time, x-coordinate, y-coordinate, heading direction, engage in attack, continue mission, and steer UAV) and two outputs (weather conditions and distance from the enemy). This model predicts the outputs regarding the weather conditions and enemy positions whenever UAV deviates from the predefined path. The model is optimized with respect to the number of rules and prediction accuracy by adjusting subtractive clustering parameters. The model is then fine-tuned with ANFIS. The final model has six rules and root mean square error value that is less than 0.05. Furthermore, to check the robustness of the model, the Gaussian random noise is added to a UAV path, and the prediction accuracy is validated.

Intelligent Systems for Structural Damage Assessment

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Our World gives several symptoms of climate change. Devastating draughts increase (negative for World (-)), global mean temperature increase (-), lightning strikes increase (-), sea ice cover melt (-), tree mortality increase (-), and forest degradation increase (-) have been observed for decades. They are all negative measures for continuity of life. Diversity of species has been decreasing, so that life on Earth is dying. Only responsible specie for this situation is humankind. This study presents a small footstep to prevent this situation. Modeling of a 100% renewable power grid on World (Global Grid) is eminent. Annual peak power load (Gigawatt: GW, Kilowatt: kW) (peak demand or load) forecasting in power demand side is crucial for global grid modeling. This study presents an experimental fuzzy inference system for the third core module (100 years’ power demand forecasting) of the first console (long term prediction) of Global Grid Peak Power Prediction System (G2P3S). The inputs (world population, global annual temperature anomalies °C) and the output (annual peak power load demand of Global Grid in GW) are modeled with seven triangular fuzzy input membership functions and seven constant output membership functions. The constant Sugeno-Type fuzzy inference system is used in the current experimental model. The maximum absolute percentage error (MAP) is calculated as 45%, and the mean absolute percentage error (MAPE) is found as 39% in this experimental study. The MAP and MAPE of the first core module model (Type 1) were 0,46 and 0,36. The MAP and MAPE of the second core module model (Interval Type 2) were 0,46 and 0,36. As a result, this study is a good start for the third core module of the first console on Global Grid Peak Power Prediction System research, development, demonstration, & deployment (RD3) project. This experimental study also warns humankind in this subject. Hopefully, the most polluting societies on our World such as China, United States, India, Russia, Japan, Germany, South Korea, and Canada take urgent actions to start to build the foundations of 100% renewable power global grid by organizing a global grid consortium.

Optimal fuzzy‐based power management for real time application in a hybrid generation system

This study presents a fuzzy-based optimal energy management scheme for a grid-tied hybrid generation system. The hybrid system under study includes a fuel cell, an electrolyser, and a hydrogen storage subsystem and is also capable of exchanging power with the local grid under hourly electricity pricing. The topic of energy management is presented in detail in the form of a non-linear constrained optimisation problem. A comprehensive mathematical formulation is applied to build an accurate model. Due to the complexity and large-scale nature of the problem, its algebraic model is given in general algebraic modelling system (GAMS). Having developed an off-line optimiser through interfacing GAMS and MATLAB, the optimal energy management problem is solved under different load profiles and the results are utilised to train a Sugeno-type fuzzy inference system that is responsible for real time energy management. The fine tuning of the fuzzy system parameters, mainly including the membership functions and the weighting coefficients, is made using subtractive data clustering. To verify the performance and validity of the proposed approach, the simulation results are presented and discussed in both off-line and on-line modes.

Dynamic Fuzzy Expert System for Multi Objective Criteria for Selection of Manufacturing Method using Sugeno Model

Objectives: Operational effectiveness is an ultimate objective of any manufacturing organization. On the shop floor the main focus is on elimination of waste and delays. The main target is designing a product for which manufacturing is a low cost process. Once organizations select appropriate tools and methods, the decision on performance measures can be taken through which progress can be monitored. The present work offers fuzzy expert system for manufacturing method selection in a dynamic environment where the organization’s objectives are subject to constant amendments. Methods/Statistical Analysis: In order to incorporate ambiguity into the manufacturing environment, the authorshave designed and developed a fuzzy expert system using Sugeno model based on the dynamic ranges of triangular membership functions for input objectives which is extremely efficient as compared to the Mamdani method in classifying vague data. In this paper, the authors proposearchitecture for fuzzy expert system based on multi objective criteria for selection of manufacturing method employing Sugeno model with constant output. Fuzzy expert system is created outside MATLAB and MATLAB is used only for creating user interface for querying methods based on objectives and for the evaluation of rules. Findings: As more than one manufacturing method may serve a single objective, each manufacturing method is encoded using a binary digit and the output is decimal representation of this binary encoding. Also, it is observed that, the number of fuzzy rules increase exponentially as a function of number of objectives. In order to cater to this problem, instead of generating so many rules the required modifications are performed at the code level to incorporate multi objective criteria. Finally a simulink model is developed for selective methods and objectives. The results obtained using Sugeno Fuzzy Expert System is compared with that obtained using Mamdani method and crisp expert system. Results: It is found that the crisp expert system and Sugeno type FIS yield similar results for classification while Mamdani type FIS offers more flexibility in method selection due to the nature of output members functions which are overlapping fuzzy sets. This yields the manager a greater freedom in method selection based on infrastructure and other resource availability.

A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems

The proposal of this study is a new nonlinear autoregressive moving average, NARMA-L2 controller, which is based on an adaptive neuro-fuzzy inference system, ANFIS architecture. The new control configuration employs Sugeno-type fuzzy inference system FIS submodels to map input characteristics to the output of a dynamic and nonlinear system. The commonly used learning algorithm, which is called a hybrid method (Backpropagation and Least Square Error), has been carried out as well as particle swarm optimisation (PSO) approach, in order to select the optimal parameters of the ANFIS submodels. Once the system has been modelled efficiently and accurately, the proposed controller is designed by rearranging the generalised submodels. The controller performance is evaluated by simulations conducted on a binary distillation column, which is characterised by a nonlinear and dynamic behaviour. The obtained results show that the PSO-ANFIS based NARMA-L2 achieved more efficient modelling and control performances when compared with other controllers. These controllers include ANN-based NARMA-L2, (PD, PI and PID like) fuzzy-tuned by GA and PSO and traditional PID, which are also implemented to the column for comparison. Stability and robustness of the proposed controller regarding system inputs variance have also been tested by applying asynchronous setpoints of both inputs of the process.

An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver

Impulsive noise is one of the main disturbances that damage the data transmission over power-line communication (PLC) systems. This paper presents an adaptive noise cancellation approach based on the adaptive neuro-fuzzy inference system (ANFIS) and a chaotic interleaver, namely ANC-CI-ANFIS scheme for impulsive noise estimation and suppression from the OFDM PLC channel. The ANFIS is based on a hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference system. Accordingly, fuzzy membership function parameters are trained using a combination of both least-square and back propagation gradient descent algorithms to emulate a given training data set. Furthermore, transmitted data are managed with a chaotic interleaver to secure data transmission and give more robustness against impulsive bursts. Simulation results are carried out on an OFDM PLC transmission chain compatible with the HomePlug AV standard under different impulsive noise scenarios. The results demonstrated the scheme's ability to detect and remove the impulsive noise from the PLC channel while keeping a high security level by using the chaotic interleaver. The major advantage of this system is its ease of implementation and faster convergence rate.

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Abstract A study on computer aided diagnosis of posterior cruciate ligaments is presented in this paper. The diagnosis relies on T1-weighted magnetic resonance imaging. During the image analysis stage, the ligament region is automatically detected, localized, and extracted using fuzzy segmentation methods. Eight geometric features are defined for the ligament object. With a clinical reference database containing 107 cases of both healthy and pathological cases, a Fisher linear discriminant is used to select 4 most distinctive features. At the classification stage we employ five different soft computing classifiers to evaluate the feature vector suitability for the computerized ligament diagnosis. Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems. The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the computer aided ligaments diagnosis.

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

In cognitive radio networks, a secondary user (SU) is dependent upon the spectrum, of varied frequency, temporarily vacated by primary users (PU) thereby making it quite a challenging task to design a protocol for finding a reliable and efficient routing path. To address the issue many routing schemes have been proposed based upon Mamdani type fuzzy inference system (FIS) to create a human intelligence analogy. The Mamdani type fuzzy inference mechanism is unable to simultaneously optimise multiple quality of service (QoS) parameters. This paper proposes a routing scheme based upon the Sugeno type fuzzy inference system (SFIS) that handles the continuously changing input parameters in quite effective manner and provides an effective performance with respect to multiple QoS parameters. The proposed routing scheme has been implemented using Matlab-9.0 and simulation results showed that the paths selected using this scheme are much more efficient than the paths selected by other routing schemes.

Adaptive Neuro-Fuzzy vibration control of a smart plate

In the present paper, the vibration supression of a smart plate with the use of ANFIS (Adaptive Neuro-Fuzzy Inference System) is investigated. The whole system consists of a nonlinear mechanical model, which is an extension of the von Karman plate model with control. The structure is subjected to external disturbances and generalized control forces. Initial and boundary conditions are set up. The initial boundary value problem is spatially-discretized by a time spectral method. The obtained discretized model is a system of nonlinear ordinary differential equations (ODEs) with respect to time. A neuro-fuzzy inference system is built and tested in order to create a nonlinear controller for the vibration supression of the plate. More specifically, a Sugeno-type fuzzy inference system is employed and trained through ANFIS. The inputs of the controller are the displacement and the velocity and the output is the control force. An effective optimization procedure is proposed and numerical results are presented.

Spatial distribution of urban trips in recently expanded Surat city through Fuzzy Logic with various clustering Techniques: A case study of typical metropolitan city in India

Abstract Trip distribution finds prime place after trip generation in sequential modelling of travel demand to cover the spatial dimensions in a geographical area, to reflect on trip length and frequency. It provides the basis for strategic land use and transport infrastructure development both at local and regional levels. Trip distribution problems in the real world are quite complex with association of uncertainty in the decision making and therefore calls for an unorthodox approach to deal with the concerned issue. Soft computing technique - Fuzzy Logic (FL) is believed to be capable of addressing the uncertainty lying in the travellers’ behaviour and has been sought to develop realistic behavioural models in the recent years. FL takes into account linguistic variables and is based on simple and logical “IF-THEN” rules which closely resemble human thought process. Fuzzy Logic based trip distribution models are developed employing Fuzzy C-mean (FCM) clustering, and are compared for their performance with the Genfis based approach, where a Sugeno-type Fuzzy Inference System (FIS) is generated using Subtractive clustering. Surat, a fast growing metropolitan city in India is considered to realize the study. The models developed here, find applications in strategic land-use and transport planning for developing Indian cities.

Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Transformer Tap Changing System

<p>Voltage control is considered one of the basic operational requirements of electrical power systems. The most popular voltage control equipment includes On-Load Tap Changer (OLTC) transformer controlled by Automatic Voltage Control (AVC) relay. Recent studies have shown that fuzzy inference systems (FIS) are applicable for transformer tap changing system. In this paper, FIS are developed for transformer tap changing system using Mamdani-type and Sugeno-type fuzzy models. The results of the two fuzzy inference systems (FIS) are compared. The basic difference between the Mamdani-type FIS and Sugeno-type FIS is included in this paper. It also shows which one is a more suitable choice of the two FIS for transformer tap changing system.</p>

Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared

Drilling optimization aims to optimize controllable variables during drilling operations, such as weight on bit (WOB), in order to improve drilling rate of penetration and reduce well costs. Prediction models of Weight on Bit (WOB) are developed using two widely-applied fuzzy inference systems (FIS), Mamdani-type and Sugeno-type, for the Ahwaz oil field and Marun gas field formations; two large producing fields in Iran. The FIS are constructed based on field data involving multiple wells; six wells from Ahwaz field, and two wells from Marun field. The controllable input variable data for the FIS includes: rate of penetration, mud weight, formation type, pump pressure, pump flow rate and rotation rate. The key difference between these two FIS techniques is the manner in which they calculate their crisp output values. The Mamdani-type FIS requires defuzzification, whereas the Sugeno-type FIS applies a constant weighted-average technique avoiding defuzzification. The results for the two field cases evaluated convincingly demonstrate that the Sugeno-type FIS is superior to the Mamdani-type FIS for WOB prediction using the same input data and membership functions. There is scope for further refinement of FIS models for WOB prediction (e.g., by adding bit type information) and the Sugeno-type FIS methods should reduce the time and cost associated with the conventional rate of penetration methods for predicting WOB.

Fuzzy C-Mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers

Flow as well as salt transport processes of coastal aquifers are dependent on the density variation of water, and are complicated and extremely non-linear in nature. Simulation of these complicated systems to an acceptable degree of accuracy using a suitable surrogate model can be useful to achieve computational efficiency in modelling. In this study, a Sugeno type Fuzzy Inference System (FIS) is developed to predict salinity concentrations at specified monitoring locations as a result of groundwater pumping. Fuzzy c-mean clustering (FCM) algorithm is used to develop the FIS. Heterogeneity of aquifer is incorporated by using different hydraulic conductivities at different layers of the aquifer. A numerical simulation model, FEMWATER is adopted to generate the required patterns of inputs and outputs for initial training of the developed FIS. The FIS analyzed different combinations of antecedent transient pumping values, and returns concentration values at different monitoring locations. The performance of the FIS for training and validation datasets is evaluated by comparing the outputs obtained from the numerical simulation model. The trained and validated FIS is utilized as an approximate simulator of the coupled flow and salt transport processes. It is then used to forecast salt concentrations at different monitoring locations. Performance evaluation results indicate that the developed FIS can be applied to forecast salt concentrations at specified monitoring locations. The FIS is able to simulate the complex physical processes of heterogeneous coastal aquifers, and can be suitable for incorporation in a coupled simulation-optimization technique to develop optimum pumping strategy.

Induction Motor Relaying Scheme for External Faults Detection and Classification Using Subtractive Clustering Based Sugeno Fuzzy Inference System

Conventional thermal relays generally suffer from inaccurate induction motor external faults identification. Recent developments in diagnosis systems have led to the scope of radically different diagnosis strategies based on artificial intelligence techniques. This study use fuzzy logic as a software tool and relay logic for external faults identification of an induction motor. In this study, a subtractive clustering based Sugeno-type fuzzy inference system is utilized to classify five external faults and the normal condition of a low-voltage three-phase induction motor using simulation and practical data sets. Five external faults- overload, over-voltage, under-voltage, single-phasing, and voltage unbalance-along with normal conditions are simulated on an induction motor over a wide range of operating voltage and load torque; sampled three-phase RMS voltages and currents are used as input feature vectors to train the fuzzy inference system. Average total classification accuracy and average root means square error obtained with ten-times random sub-sampling cross-validation of test data sets are used to find the best accurate and generalized fuzzy inference system configuration among different cluster radius fuzzy inference systems for both simulation and real-time data sets. This method accurately detects external faults with 94.37% average total classification accuracy for practical test data sets.

Prediction of convective heat transfer coefficient during deep-fat frying of pantoa using neurocomputing approaches

Abstract Deep-fat frying (DFF) is the major processing step in preparation of pantoa , a popular Indian dairy sweetmeat. In this study, the dough for pantoa was rolled into balls of 15 g, and fried in sunflower oil at 125, 135 and 145 °C for 8 min. Convective heat transfer coefficient, which defines the heat transfer characteristics of the product during DFF, was determined using one-dimensional transient heat conduction equation as 92.71–332.92 W·m − 2 ·K − 1 . Neurocomputing techniques such as connectionist models and adaptive neurofuzzy inference system (ANFIS) were compared vis-a-vis multiple linear regression (MLR) models for prediction of heat transfer coefficient. A back-propagation algorithm with Bayesian regularization optimization technique was employed to develop connectionist models while the ANFIS model was based on Sugeno-type fuzzy inference system. Both connectionist and ANFIS models exhibited superior prediction abilities than the classical MLR model. Amongst the three approaches, the hybrid ANFIS model with triangular membership function and frying time and temperature as input factors gave the best fit of convective heat transfer coefficient with R 2 as high as 0.9984 (99.84% accuracy) and %RMS value of 0.1649. Industrial relevance Convective heat transfer coefficient defines the heat transfer characteristics of a product during frying. Accurate prediction of heat transfer coefficient is important for design of process equipment and saving energy during commercial production. Developing models to predict heat transfer and the coefficients have been a challenge. Neurocomputing is one of the emerging intelligent technologies with analogies to biological neural systems. Therefore, it has the capability to predict complex relationships in food systems. Neurocomputing approaches such as connectionist and ANFIS models are now widely used in the food industry to predict various engineering properties of food, optimization of various transport processes, unit operations and formulating new products and product characteristics. No attempt has been made to predict the heat transfer coefficient during frying of pantoa . In this study, the convective heat transfer coefficient of pantoa was predicted using connectionist models and ANFIS techniques. These neurocomputing techniques are expected to alleviate the difficulties in conventional heat transfer modeling.

Condition Monitoring of Robust Damage of Cantilever Shaft Using Experimental and Adaptive Neuro-fuzzy Inference System (ANFIS)

Presently most of the failures encountered by machines are due to material fatigue. Therefore crack detection and localization is the main topic of discussion for various researchers across the globe. The dynamic behaviour of a whole structure is affected due to the presence of a crack as the stiffness of that structural element is altered. The cracks in the structure change the frequencies, amplitudes of free vibration and dynamic stability areas to an inevitable extent. In this work the effect of an open crack on the modal parameters of the cantilever shaft subjected to free vibration is analyzed. The results obtained from the experimental approach have been verified with the results obtained from finite element analysis using ANSIS 13.A methodology has been developed to predict fatigue crack propagation life of mild steel shaft. It has been assessed by adopting Adaptive Neuro-fuzzy Inference System (ANFIS), a novel soft-computing approach, suitable for non-linear, noisy and complex problems like fatigue. The proposed hybrid neuro-fuzzy system combines the learning capabilities of neural networks with fuzzy inference system for nonlinear function approximation. A single-output Sugeno-type Fuzzy Inference System (FIS) using grid partitioning has been modelled in this work. After comparing the output, it has been found that the proposed model has proved its efficiency quite satisfactorily.