ANFIS Technique Research Articles

Conjugation of deep learning and de noising data methods for short-term water turbidity forecasting

Water turbidity is a critical index of water quality due to its high correlation with the five main water quality parameters (electrical conductivity, nitrogen, dissolved oxygen, phosphorus, and pH). The exact measurement of water turbidity is a difficult process because many conditions affect the reading of turbidity. Although many researchers applied decomposition-based techniques for preprocessing, it is difficult to use these approaches in real estimation because the newly acquired data greatly affect the initial decomposed subsequent values. In this study, the threshold-based wavelet denoising method, as a data pre-processing, coupled with the deep learning models (i.e., ANN and ANFIS) was employed to enhance the performance of the water turbidity modeling. The results showed that deep learning techniques in temporal modeling of water turbidity have good accuracy and can be used with reasonable confidence. Also, data denoising increases the accuracy of deep learning methods in estimating the amount of water turbidity. ANFIS method is more accurate in both calibration and validation modes as well as in noisy and denoised conditions. Based on the results, data denoising in the ANN method has a more significant impact than in the ANFIS technique. For example, in Comb. 5, which is the best case, the improvement rate of the results in the ANN is 12% and in the ANFIS method is 4%. This could be due to the fuzzy system in handling uncertainties in the model.

An Explainable AI Approach to Agrotechnical Monitoring and Crop Diseases Prediction in Dnipro Region of Ukraine

Abstract The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of high-performance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.

Investigation on tribological characteristics of ball burnished Mg Ze41A alloy by ANOVA and ANFIS techniques

Magnesium (Mg) alloys exhibit superior strength, high castability and machinability. But, the application range of Mg alloys was limited because of low wear and corrosion resistance. Also, Mg alloys are being explored for biomedical applications due to their biocompatibility and biodegradability nature. The wear behavior of Mg alloys can be enhanced by mechanical, physical and chemical methods. This study deals with deployment of one of the mechanical methods such as ball burnishing on Mg Ze41A alloy. The contribution of each process parameter on tribological characteristics has been evaluated by ANOVA. The results reveal that burnishing force has contributed the highest with 39.16% on wear rate, 52.60% on CoF and 50.87% on interfacial temperature. The SEM images revealed wear phenomenons such as oxidation, plastic deformation. Further, the tribological characteristics at unknown test cases have been estimated by ANFIS technique. The model generated the results with an accuracy of 98.69%, 93.35% and 96.26% for wear rate, CoF and temperature respectively.

Effect of fiber content and wear parameters on abrasive wear behaviour of abutilon indicum fiber reinforced epoxy composites and its prediction using ANFIS

Applications of natural fiber-reinforced composites are growing tremendously from the last two decades, due to their exceptional properties over synthetic fiber-reinforced composites. For many engineering applications, better mechanical and tribological properties are the major requirements of polymer composites. As a result of this, investigation of the wear properties of polymer composites is an important characteristic. For this purpose, a three-body abrasive wear analysis was carried out using a pin-on disc experimental setup as per ASTM G 99 standards. The main aim of the present study is to investigate the specific wear of the abutilon indicum fiber reinforced epoxy composite with variations of fiber content, load, speed, and time. An emery polishing paper of grade 1/0 was attached to the disc to find the wear against the abrasive particles. The box-Behnken design was used to carry out the experimental trials and the results are noted down. The highest specific wear rate was observed to be 2.9 E12 at 10 ​wt% of fiber and at 30 ​N load. Wear properties were predicted using the ANFIS technique and compared with the experimental values. Moreover, SEM analysis was also carried out on the wear samples to study morphology and revealed that the worn surface contains micro-cracks, matrix debonding, and debris.

Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques

The Internet of Things (IoTs) market for consumers includes a huge variety of smart home devices. This research is conducted to investigate what factors influence customer purchase of smart home security systems. A new model is developed and the data collection is performed from the customers' reviews on the smart home security systems on Amazon.com. Totally, 12,678 reviews were collected to investigate customers' preferences in purchasing smart home security systems. To analyze the collected data, we develop a new method using text mining, unsupervised clustering, and a neuro-fuzzy system. The factors are discovered from textual reviews using the Latent Dirichlet Allocation (LDA) technique. To segment the customers' preferences the Expectation-Maximization (EM) technique was used. ANFIS technique was then used to find the relationship between the factors and customers' purchase intention. It was demonstrated that the use of text mining can be effective in the analysis of customers’ preferences from online customer reviews. The results of the data analysis are provided and discussed.

Prediction of taper angle in laser drilling of S32750 using integrated ANFIS

This paper focussed on the effect of input parameters of Nd: YAG (Neodymium: Yttrium-Aluminum-Garnet) laser cutting on Super Duplex Stainless Steel (SDSS) S32750 and the cutting quality in terms of taper angle and mechanical properties of work material with respect to laser power, cutting speed, and gas pressure.ANFIS model is used for predicting the taper angle by varying the laser drilling parameters for the smallest prediction error, an ANFIS model pimf membership function is chosen. It is also found that the ANFIS technique can predict with minimum error.

Design and Analysis of 7-DOF Human-Link Manipulator Based on Hybrid Intelligent Controller

A manipulator is an alternative to progress profitability in mechanical computerization. The robotic controller executes forms’ assembly operations in hazardous conditions. Since computerized controllers are highly vulnerable nonlinear powerful frameworks, it is hard to provide precise unique conditions at controlling laws’ configuration. Virtual Reality (VR) is a fundamental advance at use in modern biomedical, medical procedures and different fields where a 3D object helps to comprehend complex behavior. This work proposes the interaction with 3D models in VR environment achieved by accurate sensing from feedback, and then reconstructs the instruction according to practical limitation of a real human arm movement.
 In this work ANFIS played a key role in finding results with optimal values because the combination of Neural Networks (NN) benefits and obscure logic systems research examined the individual defects in both of them. Use of Artificial Neural Networks (ANN) in dynamic systems has quite extensive and accurate results, when adding a training signal system to the mixed learning base implemented at combining the slope proportions technique, a Least Square Error (LSE) preparing the ANFIS organization for any framework to take care of the issue any way. This work presents a keen controller actualization with 7-DOF controller for a model designed with a VR situation that reproduces the system design by associating Matlab/Simulink to connect the VR model with some instruction to execute orders delivered by the hybrid intelligent controller based on ANFIS technique. Palatable outcomes are implemented in reproductions that improve the procedure as an essential utilization of this controller design.

Inverse kinematics Solution of PUMA by using ANFIS Technique

In this paper Inverse kinematics solution of the PUMA 560 is solved. In robotics the main problem is to find the inverse kinematics solution. Forward kinematics is calculated with the help of D-H (Denavit-Hartenberg) parameter method. Now a day’s inverse kinematics is the area of research in robotics. In present paper, Inverse kinematics is calculated by mathematically and by ANFIS and then difference between the predicted value and deducted value is calculated. Workspace area of PUMA Robot is also shown in this paper.

RETRACTED ARTICLE: Localization approach of FLC and ANFIS technique for critical applications in wireless sensor networks

RETRACTED ARTICLE: Localization approach of FLC and ANFIS technique for critical applications in wireless sensor networks

PREDICTIVE ANALYSIS OF SURFACE FINISH IN GAS-ASSISTED ELECTRICAL DISCHARGE MACHINING USING STATISTICAL AND SOFT COMPUTING TECHNIQUES

This research attempts to explore the utilization of Gas-Assisted Electrical Discharge Machining (GAEDM) of die steel. High pressure inert gas (argon) in conventional electric discharge machining with constraint state was utilized to assess the surface roughness (SR). Analysis of Variance (ANOVA) was used to find out the process parameters that notably affected the SR. In this study, a mathematical model has been investigated to know the SR by using Buckingham pie-theorem. The fit summary confirmed that the quadratic model is statistically appropriate and the lack of fit is insignificant. Root mean square error and absolute standard deviation, obtained through response surface method (RSM), were also used for developing the model and for predicting its abilities through ANN, ANFIS. The experiment and anticipated estimates of SR during the process, obtained by RSM, dimensional analysis, ANN and ANFIS, were found to be in accord with each other. However, the ANFIS technique proved to be more fitting to the response as compared to the ANN, dimensional analysis and the RSM.

Experimental Study and ANFIS Modeling for Scour Behind Al-Shamiya Barrage

Generally, the scour behind hydraulic structures considered as an important matter the may be lead to failure. So, in this paper an experimental study and ANFIS modeling for a selected case study which is Al-Shamiya Barrage in Iraq. The M-9 device and GIS technique were used for collecting the data from field. The collection of data started from 1/12/2016 until to 1/3/2017 with monthly observation. A grid of (10 × 10) m for the 40 m length behind the floor of barrage was used to collect the experimental data. Furthermore, samples of sand from bed in that region were collected in order to implement the sieve analysis to specify the d50 and then to specify the Manning coefficient. Some equations used to calculate depth of scour behind the barrage. Then the data obtained was divided randomly into two parts: 80 % for training and 20 % for testing the model to implement the model by using ANFIS technique. The ANFIS model gave RMSE equal to 3.3965 × 10-6 m for training and 0.273 m for testing the model, respectively. This study concluded that the scour behind Al-Shamiya Barrage was clear experimentally using M-9 device and can be modeled using ANFIS model.

ANFIS models for prediction of biodiesel fuels cetane number using desirability function

Cetane number (CN) is one of the most important properties of a fuel and indicates its ignition delay in combustion process. The aim of this study is to model and predict the cetane number of biodiesel from its fatty acid methyl esters composition using ANFIS technique. The input variables were the number of biodiesel fuels’ double bonds (dn), and their molar weight (Mw). For designing ANFIS models, three fuzzy inference systems (FIS) structures were generated: grid partition, subtractive clustering and fuzzy c-means (FCM). Comparison of the developed models was performed by statistical criteria, such as coefficient of determination (R2), root mean squared error (RMSE), standard deviation of error (STD) and mean absolute percent error (MAPE) coupled with desirability function. The obtained results showed that the maximum coefficient of determination is related to grid partition FIS (pimf) and ranges from 0.939 to 0.951 for various data. The minimum values of RMSE, STD and MAPE criteria varied in 3.62–3.91, 2.85–3.92 and 0.53–5.19 ranges, respectively. According to the obtained ANFIS models, it can be concluded that all models have a good potential to determine the cetane number of biodiesel fuel. Consequently, the results showed that the ANFIS models developed by grid partition FIS (pimf) and fuzzy c-means (FCM) technique have a higher final desirability of 0.857 and 0.718, respectively.

Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration

Refrigeration systems exist in different branches of industry and are characterized as great energy consumers with considerable nonlinear behavior. Several studies have promoted energy costs reduction and minimization of nonlinearities effects in such systems. Model predictive control has been successfully used to stabilize processes in the presence of such nonlinearities; therefore, its application in refrigeration systems is considered promising. In the present study, Takagi–Sugeno models were developed and validated in order to predict the evaporating and secondary fluid temperatures (TE and TP) based on the ANFIS technique (Adaptive Network-based Fuzzy Inference Systems) for a vapor-compressor chiller equipment. The prediction performance of resulting models was analyzed and accessed based on the variance accounted for criteria. These models were then used as the basis for prediction models in several generalized predictive controllers (GPC) denoted here as GPC-ANFIS controllers. Different predictive controllers were designed for different local rules (Fuzzy rules) and the global control action was assumed as the weighted sum of local controllers. Experimental tests considered two distinct controllers, namely the GPC-ANFISTE (evaporating temperature control by means of compressor speed variation) and GPC-ANFISTP (propylene glycol temperature control by means of compressor speed variation), were performed. The experimental tests for setpoint tracking (±1°C) considering 3000 W of constant heat load showed satisfactory results with setpoint deviation around ±0.3°C. Therefore, the ANFIS technique demonstrated to be able to provide reliable predictive models to be used in generalized predictive control algorithms.

An Innovative Remote Sensing Image Retrieval Techniques Based on Haar Wavelet-LTRP and ANFIS

In the existing Remote sensing image retrieval methods, the images are extracted from the remote sensing image database by means of three characteristic models they are visual features, object features and the scene feature. Although this technique achieves high retrieval accurateness, the precision value is low down. In order to enhance the efficiency and precision value even more higher a Haar wavelet based LTRP technique is proposed in this paper. To extract the object feature instead of earlier new watershed segmentation(NWS) method, Haar wavelet based LTRP technique is used. In the proposed technique, at first the visual features are removed from the images by means of the spatial spectral heterogeneity technique. Afterwards the object features are removed by applying the Haar wavelet and the object features are extracted from the wavelet band by developing LTRP method and the Extracted object features are classified by Neuro-Fuzzy system typically known as ANFIS. Subsequently Scene semantic models are used for the recovery of parallel scene images from the database. The projected RSIR system based on Haar wavelet-LTRP and ANFIS technique are executed in working platform of MATLAB. The performance is measured by utilizing a compilation of remote sensing images taken from the database. In addition the recital is examined by comparing the projected Haar wavelet-LTRP method with the NWSRSIR and the usual SBRSIR method in terms of performance estimate metrics such as precision, recall and F-Measure rate. The implementation effects show the competence of projected Haar wavelet based LTRP method in Remote sensing image retrieval method.

A new congruency-based hysteresis modeling and compensating of a piezoactuator incorporating an adaptive neuron fuzzy inference system

This paper proposes a new approach to modeling and compensating for a rate-independent hysteresis of a piezoactuator. The model—namely, congruency-based hysteresis—is developed based on two very important characteristics of the hysteresis. These are congruency and wipe-out. The proposed approach consists of two branches for cases of monotonic increase and monotonic decrease of input excitation. In order to realize this model, datasets of first-order minor-loop values should be determined in advance. This can be done using the adaptive neuron fuzzy system (ANFIS) technique and experimental data. With this technique, an input-output relationship of first-order minor-loop values is estimated effectively. In addition, the ANFIS technique is also used in constructing datasets of inverse first-order minor-loop values, which are essential parts of a congruency-based hysteresis compensator. Several experiments in modeling and open-loop control are conducted to show the effectiveness of the proposed approach. In addition, a comparative work between the proposed approach and one of previous works is undertaken to demonstrate the benefit of the proposed method.

A Comparative Analysis on Controlling Speed of Universal Motor by using Fuzzy and ANFIS Controller

ines have comforted human beings in every walk of life such as scientifically, economically as well as socially. Machines consists of immotile as well certain rotating parts which combine to reduce human efforts into well-founded and pertinent work for example pulleys, blenders,, compressors etc. For every advanced technology of machines its different parameters are needed to be controlled for a competent, productive and faultless system. This paper aims on careful investigation of two advanced type of approaches for controlling speed of universal motor. These two approaches namely Fuzzy technique and ANFIS technique are employed as powerful mechanisms for varying speed based on output graph of step response versus time. A comparative evaluation is then done on the basis of two most important parameters of machines named as percentage overshoot and settling time. Comparative analysis aids in determining the best methodology to be applied in everyday applications for producing unerring, authenticated and unimpeachable systems. Keywordsmotor, Fuzzy Controller, ANFIS controller, Percentage overshoot, Rise time

Anfis to estimate discharge capacity of rectangular side weir

The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangular side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a rectangular sharp-crested side weir, the discharge coefficient in the side weir equation needs to be determined in accordance with the effective dimensionless parameters F1 (Froude number), L/b (weir length/channel width), L/h1 (weir length/flow depth), and p/h1 (weir height/flow depth). The discharge coefficient of rectangular side weirs was determined using 843 laboratory test results. The performance of the Anfis model is compared with multilinear and nonlinear regression models. The criteria used for the evaluation of the performance of models are root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics. Comparison results indicated that the Anfis technique could be successfully employed in modelling discharge coefficients. It is found that the Anfis model with RMSE of 0·043 in the test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0·054 and 0·106, respectively.

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.

Estimation of subsurface strata of earth using Adaptive Neuro-Fuzzy inference system (ANFIS)

Soft computing techniques are widely used for the applications on most of the nonlinear problems related to the real world. Earth’s most of the nonlinear characteristics exhibit the uncertainty problem that has to be interpreted with most of the advanced soft computing tools. Here the three layer electrical resistivity data has taken for interpreting the subsurface parameters of the earth using Adaptive Neuro-Fuzzy inference (ANFIS) technique. ANFIS can be predictably used for most of the nonlinear problems. Its membership functions and rules with adjustable parameters will help the interpretation technique with less error percentage results. In the present study, the program is specially designed for the interpretation of three layer electrical resistivity data. The network model is successful in training with large number of data sets available. Interpretation using ANFIS technique will give the promising results with good accuracy. With much less error percentage, the program supports all types of three layer electrical resistivity data more than a conventional method can do. Typical problems with parameter estimation can be done more efficiently with this ANFIS program.