Application Of Adaptive Neuro-fuzzy Inference System Research Articles

Application of ANFIS in Decision-Making on the Smart Control Early Warning System for Tornadoes

A tornado is one weather process that arises due to atmospheric instability. A tornado is a strong wind, but not all strong winds are tornadoes. Tornadoes have a short time frequency but can result in no minor disaster because they can blow objects away and uproot trees. Due to the consequences, an early warning system is needed as an anticipation for the community in the affected areas so that it can help the community by warning early on of the occurrence of a tornado. The ANFIS (Adaptive Neuro Fuzzy Inference System) method is used to forecast the event of a tornado, and the parameters used are wind speed, ambient temperature, and ambient humidity. This study will compare the ANFIS method using hybrid and backpropagation algorithms. Using the backpropagation algorithm, an error of 0.42385 was produced during training and testing, and an average error of 136.54 was obtained. When using the hybrid algorithm, the error during training is 2.0781 x 10-5, and the average error during testing is 0.015%.Keywords — ANFIS ; Anemometer; DHT22; Early Warning System; Tornado.

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Application of ANFIS and ANN for Partial Discharge Localization in Oil Through Acoustic Emission

This paper presents an examination on the acoustic partial discharge (PD) localization in oil based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit was used to measure the electrical PD. The acoustic PD was obtained through an acoustic emission (AE) sensor and pre-amplifier gain unit. In total, 112 coordinates for each of the AE sensors were utilized to evaluate the location of the PD. Once the voltage reached 30 kV, the electrical and acoustic PDs were recorded. Next, the data were pre-processed by moving average and analyzed by time-of-arrival (TOA), ANFIS and ANN. The distance between PD and AE sensor were calculated based on TOA to determine the PD location. These information were used as an input to train the network by optimizing epoch and neuron for ANFIS and ANN in order to locate PD. ANFIS has the best percentage of PD source prediction based on root mean square error (RMSE) and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) as compared to ANN. Meanwhile, the computation time for ANN is 1.75 s faster than ANFIS to perform PD localization based on acoustic emission PD signals.

Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method

This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works.

Application of ANFIS, ELM, and ANN models to assess water productivity indicators based on agronomic techniques in the Lake Urmia Basin

Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe.

An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers

Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques.

Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences.

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

Application of ANFIS, ANN and fuzzy time series models to CO2 emission from the energy sector and global temperature increase

PurposeA significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.Design/methodology/approachThese three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO2) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.FindingsA performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO2 emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.Social implicationsThe paper provides more awareness about fuzzy techniques application in CO2 emissions studies.Originality/valueThese techniques can be extended to other models to assess the impact of CO2 emission from other sectors.

Fault Diagnosis for Transmission Lines Systems Using ANFIS Techniques

This paper presents a fault diagnosis for long transmission lines using Adaptive Neuro-Fuzzy Inference System (ANFIS). The electric power transmission system is a link power generation and distribution. If a failure occurs as long the transmission line could be estimation caused of undesired fault power delivery to consumer come not go well. Therefore, it would need to provide an alternative solution to solve this problem. The objectives of this paper are classification and estimate of a fault into the transmission line by using application of ANFIS. The systems have been put forward and tested on simulated data transmission lines into different faults. The results test given to contribute to an alternate technique where it has good performance for fault diagnosis in the transmission lines.

Application of ANFIS for machinery noise prediction in a bauxite mine

The aim of this study is to predict the sound pressure level (SPL) values from ISO 9613-2, arti?cial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) model. For this reason, sound pressure levels produced from the machinery operations were measured in a highly mechanized opencast bauxite mine in India. After collecting the ordered pairs of sound power level, distance, temperature, relative humidity and wind speed, SPL values were determined using ISO 9613-2 method. An endeavour has been made to examine the applicability of the developed soft computing models to predict SPL. To accomplish this objective, data obtained from the machinery operation measurements were assessed by constructing an ANFIS based prediction model. Sound power level (SWL), distance, temperature, relative humidity and wind speed were chosen as the input parameters and SPL as the output of the developed model. SPL prediction capability of the constructed ANFIS model has proved to be effective in terms of performance indices such as root mean square error (RMSE) between anticipated and measured SPL values. Prediction performance comparison has been made between the measured, ISO 9613-2 model, ANN and ANFIS model. The results indicated that the proposed ANFIS model exhibited better prediction performance when compared with ANN prediction model.

Application of ANFIS and linear regression models to analyze the energy and economics of lentil and chickpea production in Iran

In the present study, the energetic and economic modeling of lentil and chickpea production in Esfahan province of Iran was conducted using adaptive neuro-fuzzy inference system (ANFIS) and linear regression. Data were taken by interviewing and visiting of 140 lentil farms and 110 chickpea farms during 2014-2015 production period. The results showed that the yield and total energy consumption were calculated 2,023 kgha-1 and 32,970.10 MJha-1, respectively for lentil; and 2,276 kg ha-1 and 33,211.18 MJ ha-1, respectively for chickpea. Energy use efficiency was found to be 0.9 for lentil and 1.02 for chickpea; while benefit-cost ratio (BCR) were obtained 1.60 for lentil and 1.74 for chickpea. Regression results demonstrated that the coefficient of determination (R2) were 0.92 for lentil and 0.89 for chickpea. In adittion, in regression estimated model in terms of BCR, R2 were obtained as 0.86 for lentil and 0.72 for chickpea. In modeling of yield using the best ANFIS model, R2 were calculated 0.99 and 0.98, respectively for lentil and chickpea. Finally, for evaluation of crops BCR by best ANFIS model, R2 were determinate as 0.94 and 0.91 for lentil and chickpea, respectively. It was concluded that ANFIS model could better predict the energy output and BCR than that of linear regression model.

Soil Disturbance Evaluation: Application of ANFIS

In this work, an Adaptive Neuro-Fuzzy Inference System (ANFIS) applied for study the contiguous relations between soil disturbed indicators. Several ANFIS surfaces, which described the contiguous relations between individual soil disturbed indicators (DFSs), values of soil parameters and depth, as well as the contiguous relations between DFSs and combined indicator of soil disturbance (DFC), were obtained.

Pore diameter of nanoporous anodic alumina: Experimental study and application of ANFIS and MLR

This paper presents numerical and experimental results to show the effect of nonlinear models has on the prediction of pore diameter of nanoporous anodic alumina (NPAA). For this purpose, a database of pore diameters was collected from the literature to developing two models: (1) an adaptive neuro-fuzzy inference system (ANFIS) model and (2) a multiple linear regression (MLR) model. Inputs for these two models compromised of the concentration, the temperature and the voltage whereas pore diameter in a NPAA membrane was the target output. To illustrate the accuracy of two proposed models, a comparison between outputs obtained from the models, test results and two empirical formulas has been made. The comparison reveals that the models have a good capability of predicting pore diameters with an acceptable error with respect to empirical formulas.

MODELING RESERVOIR WATER RELEASE DECISION USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Reservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir. Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoir’s upstream areas. Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate. Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfall’s location. In addition, rainfall recorded at different locations may cause fuzziness in the data representation. Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model. ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall. In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study. Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted. Both models utilized the spatial temporal pattern of the rainfall as input. The first model considered the current reservoir water level as an additional input, while the second model retained the existing input. The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.

Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution

In this work, cationic (Mg2+, Ca2+, Sr2+, and Ba2+) substitution in X and Y faujasite zeolite structures and their effects on capacity of zeolites for methane adsorption were studied by applying multiple linear regression and expert adaptive neuro-fuzzy inference system (ANFIS) . Temperature, pressure, and molecular weight of cations were used as the input parameters. The results obtained from application of the proposed ANFIS model showed that at high pressures, the zeolite with smaller cation in their structure have higher methane adsorption capacity. The root-mean-square error, square correlation coefficient (R2), mean absolute error, and percentage of mean absolute relative error for X and Y faujasite zeolites were evaluated, which indicated that ANFIS model can accurately predict the adsorption of methane gas on X and Y zeolites in the presence of the substituted cations.

Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology

Dissection of the power output in a row of working turbines and the reliance on wind course in respect to the column heading is examined. The point is to portray the extent of the wake impacts and give a sign of the indication of wind bearing. The target is to delineate whether the uniting of wakes inside extensive wind homesteads can be depicted by basic direct models or whether the consideration of the two-route collaboration between the wind turbines and the limit layer is a fundamental essential for precise models of wakes to be utilized within future wind farm plan. Soft computing methodologies may be utilized as substitute for analytical approach since they provides some benefits such as no need to information of internal system parameters, compact solution for multi-variable problems. This investigation dealt with application of ANFIS (adaptive neuro-fuzzy inference system) for predicting the wake power and wind speed deficit. To provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r) were utilized. The study results suggested that ANFIS would be an efficient soft computing methodology to offer precise predictions of wake wind speed deficit and power deficit ratio in wind farms. According to the achieved results, the best prediction was observed for free wind speed of 8 m/s. The RMSE, R2 and r were computed as 0.0763, 0.9893 and 0.9946 for ANFIS prediction of wake wind speed deficit and as 0.0128, 0.9967 and 0.9984 for ANFIS prediction of power deficit ratio.

Application of ANFIS in Engine Fault Prediction

Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in engine fault prediction is introduced in this paper. For insuring the engine safety and reliability, the fault of engine must be predicted, and the fault can be terminated in time. So, the fault prediction theory and the method of ANFIS are studied. The fault prediction sets, the structure and parameters for ANFIS are given in the course of fault prediction. It has made very pleased result through experiment of certain type engine fault prediction, and it is important to engine maintenance. The application of ANFIS has a good reference to engine fault prediction researching.

Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems

Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.

Neuro-fuzzy modelling and forecasting in water resources

Recently, with rapid development in computer science and technology, artificial intelligent (AI) models that emulate human thinking ability and brain structure are increasingly used in hydrological forecasting context. Neuro-fuzzy (N-F) models or specifically adaptive neuro-fuzzy inference systems (ANFIS) are rapidly becoming conventional in either academic or industrial applications. Although, there is a common network structure among ANFIS models, there is no one-fits-all ANFIS architecture for every case. Moreover, it is discussed that in many application, theory does not guide in model building process by either suggesting the relevant model input variables or correct functional form and model configuration. This paper is focused on the application of ANFIS in water resources context and reviews the common architecture of ANFIS models been used in this area of research. The aim is to familiarize the new researchers with ANFIS application process in water resources studies. Key words: Neuro-fuzzy modelling, adaptive neuro-fuzzy inference systems (ANFIS), hybrid learning algorithm, subtractive clustering.