Fuzzy Inference System Technique Research Articles

A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Ensuring the reliable and energy efficient data routing in Wireless Sensor Networks (WSNs) is still remains one of the challenging and demanding tasks due to its dynamic architecture. For this purpose, the different types of routing methodologies and security schemes have been developed in the conventional works. However, it faced the problems related to increased network overhead, high cost consumption, reduced Quality of Service (QoS), and inefficient bandwidth utilization. The main contribution of this work is to implement an optimization based secured routing methodology for establishing an energy efficient data communication in WSNs. For clustering the nodes, the parameters such as residual energy, trust score, and mobility have been considered, which also helps to simplify the networking operations. Moreover, the outliers in the network are detected with the help of Spatial Temporal Fuzzy Inference System technique, which generates the set of inference rules based on the distance, energy, and mobility measures. Also, the Crow-Whale Optimized Secured Robin Routing (CWOSRR) is used to enable the data transmission in the network through the optimized path, which avoids the loss of information. During the simulation analysis, the results of the proposed technique is validated and compared by using various performance measures.

Optimizing density, dynamic viscosity, thermal conductivity and specific heat of a hybrid nanofluid obtained experimentally via ANFIS-based model and modern optimization

In this study, rGO/Co3O4 nanocomposite was synthesized, characterized, and then the thermophysical properties were obtained experimentally, after which the experimental data at varying values of temperature and particle loadings was used for optimization purposes. The study was concerned with different values of the controlling parameters. The in-situ/chemical reduction technique was used to synthesize the rGO/Co3O4 nanocomposite and then characterized with x-ray diffraction, transmission electron microscope, and magnetometry. The system was studied at temperature values ranging at 20, 30, 40, 50, and 60 °C and with particle loadings of 0.05%, 0.1%, and 0.2% wt%. The authors in this article have introduced a novel population-based algorithm that is known as Marine Predators Algorithm to obtain the optimal values of the controlling parameters (i.e., temperature and nanofluid mixture percentage) that minimize two controlled variables (i.e., density and viscosity) as well as maximize the other two controlled variables (thermal conductivity and specific heat). The rGO/Co3O4 nanocomposite nanofluid thermal conductivity and viscosity were investigated experimentally, and a maximum increment of 19.14% and 70.83% with 0.2% particle loadings at 60 °C was obtained. At 0.05%, 0.1%, and 0.2% particle loading wt%, the density increased by 0.115%, 0.23%, and 0.451% at a temperature of 20 °C; simultaneously, density increased by 0.117%%, 0.235%, and 0.469% at 60 °C, respectively as compared to water. At 0.2 wt%, the maximum decreased specific heat was 0.192% and 0.194% at 20 °C and 60 °C. When compared with water, no effect was observed with an increase in temperature/: a similar trend as that of the water was followed. The optimal values were found to be at a temperature of 60 °C and for 0.05% particle loading of the prepared nanofluid. However, among the conducted experiments, the optimizer pointed out that the optimal experiment was the one conducted at a temperature of 60 °C and a nanofluid percentage at 0.05. In conclusion, the proposed methodology of modelling with an artificial intelligence tool such as an adaptive network-based fuzzy inference system technique and then determining the optimal parameters with the marine predators algorithm accomplished the goal of the study with major success.

Spatial Modelling to Optimize the Positioning of Air Defense Surveillance Radars with Minimal Shadow Zone

Indonesia’s strategic position between two continents and two oceans has resulted in an obligation for Indonesia to be involved in various international cooperation and international conflicts. The declaration of the development of the maritime sector to be the cornerstone of Indonesia’s main strength requires support from the air defense sector. The current condition of Indonesia’s air defense sector, especially the Air Surveillance Radar has not worked optimally resulting in an air defense operation that has not been maximized. The properties of high-frequency electromagnetic waves used in Doppler radar have a straight line of sight. Mountains, hills, and artificial objects/buildings can become obstacles, create blank areas (areas that cannot be covered by radar) behind them, and reduce the performance of the radar coverage. The use of sensing technology, in particular, the geographic information system to determine the position of air surveillance radar placing is an effective solution to optimize the coverage radars exist. The rectangular rule approach method combined with the land surveying technique is a combination of methods that can be used to measure the volume of air defense radar coverage. The level optimization of radar position planning point can be determined by processing the obstacle volume, land slope, earthquake and landslide vulnerability, and land cover conditions into the calculation of the fuzzy inference system technique. This study examines the concept of using Geographic Information Systems to measure the level of radar optimization when placed in a planned position. This research aims to produce a method review on optimizing the performance of air surveillance radar coverage by utilizing remote sensing technology to determine the radar placement position.

Prediction of RO Membrane Performances by Use of Adaptive Network-Based Fuzzy Interference Systems

This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. The result indicated that this method could predict the performance of the RO membrane faster and more accurately than previous numerical techniques. The squared correlation coefficient between real data and predicted data of this technique was 0.9973 for separation factor, 0.9916 for NP and 0.9975 NT, which are better in comparison with numerical methods, and previous Artificial Neural network used by the author to model these membranes. It was observed that the squash factor, reject ratio, and accept ratio has no significant effect on ANFIS performance. Results showed that for all cases better performances achieved when this parameter has a value of more than 0.5, as 0.86 for separation factor, 0.91 for net pre flux, and 0.83 for total flux. This technique just takes a few seconds to model RO membrane performance which is very faster than other numerical methods. So, this technique could be a powerful method to predict RO membranes.

Comparing fuzzy rule-based MPPT techniques for fuel cell stack applications

The process of maximum power extraction from alternative energy systems was at the first instance applied to systems such as photovoltaic and wind power technologies. Both systems operate on the principle of conversion of either solar or wind energy into electrical energy. Thus, the generated powers depend directly on the solar radiation level and the cell temperature for photovoltaic systems and on the wind speed for wind generators. To avoid inefficient system operations, the power outputs are optimized through maximum power point tracking techniques. Fuel cells are also candidates for maximum power extraction as their output powers are affected by internal limitations and operating parameters. At each operating time, the system needs to be constrained through MPPT controllers to provide continuously as much power as possible to avoid low efficiency and excessive fuel use. Various techniques can be employed for MPPT controllers design. This paper investigates MPPT controllers based on fuzzy inference system techniques, the objective is to compare Mamdani and Sugeno controllers’ time response. The investigation is conducted on a 50 kW PEMFC stack coupled to a power electronics converter and a DC load. The simulation is carried out under Matlab/Simulink environment.

Decentralized stable and robust fault-tolerant PI plus fuzzy control of MIMO systems: a quadruple tank case study

Abstract This paper presents a typical design of fault-tolerant control using two decentralized PI plus fuzzy controllers to control the level of the lower two tanks in a nonlinear quadruple tank level process (QTLP). We also present some basic aspects of decentralized control design concerning stability and performance and illustrate them on a case study: a virtual model of a quadruple tank process. Control structure selection based on performance relative gain array (PRGA) is used, and its ability to evaluate the achievable performance is discussed. The controllers are designed based on a conventional PI controller plus fuzzy inference system technique. The relation between inputs/outputs was proved using relative gain array (RGA), and then, we divided the quadruple tank system into two subsystems and controlled each of them separately, both in minimum and non-minimum phases. Both the controllers were designed to control the nonlinear QTLP at any operating points. The proposed approach was compared with a decentralized fuzzy controller subject to actuator/sensor and system component faults. The simulation results show that the proposed decentralized fault-tolerant PI plus fuzzy controller has a more accurate tracking level and less computational time in both minimum and non-minimum phases.

Incipient Fault Diagnosis in Stator Winding of Synchronous Generator: A CMFFLC Technique

ABSTRACTIn the paper, a combined strategy of moth–flame optimization (MFO) algorithm and fuzzy logic controller (FLC) for incipient fault diagnosis of the synchronous generator is proposed. The motivation behind the proposed topology is to analyse the beginning issues exhibited by an asynchronous generator under different situations like healthy and unhealthy conditions. Initially, a synchronous generator is assessed in the ordinary condition and from that point onwards, fault is made in the synchronous generator and the framework practices are checked and signals are measured which can be viewed as mis-shaped waveforms. For the collection of data-set from the input current signal, MFO is presented which extracts the signal and structures the possible data-sets. In light of the fulfilled data-set, the FLC performs and diagnoses the kind of fault that has happened in the stator winding of the synchronous generator. In order to evaluate the effectiveness of the proposed method, the incipient faults are analysed. The proposed technique is implemented in MATLAB/Simulink platform and this is approved utilizing execution measures, for example, accuracy, precision, recall, and specificity. Likewise, the proposed method is analysed with factual measures, for example, the root mean square error, mean absolute percentage error, mean bias error, and consumption time; and the execution is evaluated by utilizing the examination at various strategies like artificial neural network, fuzzy, and adaptive neuro fuzzy inference system techniques.

Aplikasi Fuzzy Inference System untuk Menentukan Lokasi Pengembangan Sentra Peternakan Rakyat (SPR) Sapi Potong di Kabupaten Jember

The development of community farming center (SPR) of cattle beef to consolidate the potential of people's cattle beef to be managed optimally to increase the production and welfare of farmers in realizing national beef self-sufficiency. Jember Regency as one of the centers of cattle beef in East Java Province has a policy to develop SPR in a number of sub-districts but still faces a number of problems that is determining which district will be chosen as SPR location. This research aims to design the model of determining SPR cattle beef location using fuzzy inference system technique. A number of key criteria in location determination are explored then designed into decision support models using the Matlab fuzzy tool box. The results showed that there are three key criteria for determining the location of SPR development of cattle beef, namely 1) potential Feed from plants; 2) population of cattle beef; and the availability of facilities and infrastructure. Decision support model is built with fuzzy inference system technique using Triangular Fuzzy Number (TFN) representation model and MAMDANI inference method. The number of rules built as many as seven rules with the operator or and and and. Model simulation results show that the locations that are categorized as Very Suitable for SPR development of cattle beef are Silo, Sumberjambe, and Ledokombo Subdistricts, While sub District Gumukmas, Sumberbaru, Puger, Wuluhan, Kencong, Bangsalsari, Mumbulsari, and Jenggawah, fall into the Suitable category.

Adaptive Neuro Fuzzy Inference System Technique on Polysomnographs for the detection of Stressful Conditions

ABSTRACTA method for computerized detection of heat stress is presented in this paper and tested on pre-recorded data from a range of subjects. The physiological changes that happen in the subjects are incorporated as fuzzy logic to distinguish the stress level of the subjects as chronic or acute stress. The sleep stage classification is done initially with the help of pre-established rules governed by American Academy of Sleep Medicine. After the sleep stage classification is done, data are further classified as chronic or acute stress with respect to their controlled states. The proposed algorithm employs adaptive neuro-fuzzy inference system and Mamdani fuzzy model and achieves an average classification accuracy of 89% in detecting stress levels.

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

Fuzzy Logic Application for House Price Prediction

Various methods have been used previously to estimate housing sales prices to model the underlying non-linearity relation between housing attributes and the price of property. More advanced non-linear modelling techniques such as Artificial Neural Networks (ANN), Fuzzy Inference System (FIS) and Fuzzy Logic (FL) emerged recently to model the nonlinear relation between the independent variables and the price function. A new structured model for house prices prediction based on Fuzzy Logic is proposed. A fuzzy logic based regression model has proved to be effective to address many prediction problems used in business forecasting, marketing and insurance. This paper highlights the development of a theoretical formulation for sales price prediction through the utilisation of a fuzzy regression model by applying fuzzy logic and fuzzy inference system techniques. The results show favourable outputs which indicate superior prediction function when compared with ANN and FIS as well as indicate the fuzzy functional relationship between dependent and independent variables.

Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique

Accessibility and accurate estimation of the tropospheric delay plays a crucial role in meteorological studies and weather forecasts as well as improving positioning accuracy. We propose to employ an adaptive neuro fuzzy inference system (ANFIS) to build estimation and prediction models for zenith path delay (ZPD). Five selected stations over Antarctica were used to examine the applicability of ANFIS. GPS ZPD data of 2010 with five-minute resolution was used as the target output. A fuzzy clustering algorithm is adopted to enhance the performance of the models, which is able to minimize the number of membership functions and rules for better efficiency in the models. To investigate the accuracy of models developed, a combination of the surface pressure (P), temperature (T) and relative humidity (H) is performed to obtain the best estimation of ZPD. The results demonstrated that ANFIS models with three inputs network (P, T and H) agreed very well with ZPD obtained from GPS than separated input only coming from P or T, or P and T, or P and H. Finally, the input network (P, T and H) is selected in developing the ZPD predictive models. The prediction resulted from one-step to eight-step ahead development, demonstrated that the high-resolution of data used in training process will increase the accuracy of the predictive model.

Sensorless Control of PMSG Wind Turbine Using ANFIS

This paper deals with the control of permanent-magnet synchronous generator (PMSG) where the rotor position and speed estimation is essentially required to operate it on maximum power points. Here, Adaptive Neuro Fuzzy Inference System Technique (ANFIS) is used to estimate the speed and position of PMSG, where an ANFIS-system is continuously tuned with actual value of PMSG. The proposed system consists of a back-to-back converter, where PMSG side converter has MPPT output and another one is used for grid synchronization. The generator side ANFIS estimator is used to estimate the rotor position and speed accurately over a wide speed range. The purpose of grid side ANFIS estimator is to control and manage the inverter according to the load demand and storage of electricity in battery which is coupled with dc link of back to back converter respectively. The stored electricity in battery is fed back to grid when needed. The proposed system is designed in MATLAB/SIMULINK.

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Wheat, barley, sugerbeet, potato, alfalfa, and corn are common crops produced in Iran, which need the most virtual water volume compared to other crops. Determination of the virtual water for these crops would assist in better management of water resources. The main objective of this study is to find out the best technique for estimating and mapping of virtual water. In this research, the virtual water volume was determined by crop water requirement and crop yields using three ANN structures as well as ANFIS technique. Based on RMSE and R the comparison of obtained results predicted through the applied ANNs structures indicate that the RBF outperforms the other models for estimating virtual water for wheat, potato, corn, and barley. Moreover, a comparison between RBF and ANFIS revealed that ANFIS is a promising model, which can be efficient mathematical tool for estimation of crop‟s virtual water.

Forecasting Financial Time Series Using Multiple Regression, Multi Layer Perception, Radial Basis Function and Adaptive Neuro Fuzzy Inference System Models: A Comparative Analysis

In the last few decades, techniques such as Artificial Neural Networks and Fuzzy Inference Systems were used for developing predictive models to estimate the required parameters. Since the recent past Soft Computing techniques are being used as alternate statistical tool. Determination of nature of financial time series data is difficult, expensive, time consuming and involves complex tests. In this paper, we use Multi Layer Perception and Radial Basis Functions of Artificial Neural Networks, Adaptive Neuro Fuzzy Inference System for prediction of S% (Financial Stress percent) of financial time series data and compare it with traditional statistical tool of Multiple Regression. The accuracies of Artificial Neural Network and Adaptive Neuro Fuzzy Inference System techniques are evaluated as relatively similar. It is found that Radial Basis Functions constructed exhibit high performance than Multi Layer Perception, Adaptive Neuro Fuzzy Inference System and Multiple Regression for predicting S%. The performance comparison shows that Soft Computing paradigm is a promising tool for minimizing uncertainties in financial time series data. Further Soft Computing also minimizes the potential inconsistency of correlations.

Soft Computing Approaches for Prediction of Software Maintenance Effort

The relationship between object oriented metrics and software maintenance effort is complex and non-linear. Therefore, there is considerable research interest in development and application of sophisticated techniques which can be used to construct models for predicting software maintenance effort. The aim of this paper is to evaluate and compare the application of different soft computing techniques – Artificial Neural Networks, Fuzzy Inference Systems and Adaptive Neuro-Fuzzy Inference Systems to construct models for prediction of Software Maintenance Effort. The maintenance effort data of two commercial software products is used in this study. The dependent variable in our study is maintenance effort. The independent variables are eight Object Oriented metrics . It is observed that soft computing techniques can be used for constructing accurate models for prediction of software maintenance effort and Adaptive Neuro Fuzzy Inference System technique gives the most accurate model.

A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.