Multiple Adaptive Neuro-fuzzy Inference System Research Articles

Prediction of Downpull Force Coefficient on the Underflow-Type Floating Gate

The experiment was conducted to compare the hydraulic openness by the buoyancy theory of the hydrostatic pressure and the hydraulic openness by the measurement, and the difference between the theory and the measured hydraulic openness was confirmed to be the effect by the downpull force. As a result of using the existing theoretical formulae to examine the downpull force of the buoyancy-type floating gate, it was found that the existing formulae were difficult to apply to the buoyancy-type floating gate. Therefore, this paper proposed the use of the multiple regression analysis and adaptive neuro-fuzzy inference system method for predicting downpull force coefficient of the buoyancy-type floating gate. The experimental results indicate that the downpull force coefficient decreased as the opening ratio increased. In addition, the downpull force increased as the opening ratio increased below the opening ratio of 0.300, however the downpull force decreased as the opening ratio increased from the opening ratio exceeding 0.300. Finally, prediction results by using the proposed relationship indicated that the errors are much smaller compared to the results of the existing theoretical formulae. In addition, the application of the adaptive neuro-fuzzy inference system method improved the prediction significantly, reducing the errors to the half of that by the existing theoretical formulae.

Investigation of damaged structure in altered viscous fluid medium using multiple adaptive neurofuzzy inference system (MANFIS)

Investigation of damaged structure in altered viscous fluid medium using multiple adaptive neurofuzzy inference system (MANFIS)

The combination of multiple linear regression and adaptive neuro-fuzzy inference system can accurately predict trihalomethane levels in tap water with fewer water quality parameters

Artificial Neural Network (ANN) models are accurate in predicting the levels of disinfection by-products (DBPs) in drinking water. However, these models are not yet practical due to the large number of parameters involved, which should take a significant amount of time and cost to detect. Developing accurate and reliable prediction models of DBPs with fewest parameters is essential in the management of drinking water safety. This study used the adaptive neuro-fuzzy inference system (ANFIS) and radial basis function artificial neural network (RBF-ANN) to predict the levels of trihalomethanes (THMs), the most abundant DBPs in drinking water. Two water quality parameters identified by multiple linear regression (MLR) models were used as model inputs, and the quality of the models was assessed based on criteria such as correlation coefficient (r), mean absolute relative error (MARE), and the percentage of predictions with absolute relative error less than 25% (NE<25%) and over than 40% (NE>40%), etc. The results showed that the ANFIS models had higher correlation coefficients (r = 0.853–0.898) and prediction accuracy (NE<25% = 91%–94%) compared to RBF-ANN models (r = 0.553–0.819; NE<25% = 77%–86%) and traditional MLR models (r = 0.389–0.619; NE<25% = 67%–77%). Conversely, the prediction error, as indicated by MARE and NE>40%, showed the opposite trend: ANFIS models (MARE = 8%–11%; NE>40% = 0–5%) < RBF-ANN models (MARE = 15%–18%; NE>40% = 5%–11%) < MLR models (MARE = 19%–21%; NE>40% = 11%–17%). The present study provided a novel approach for constructing high-quality prediction models of THMs in water supply systems using only two parameters. This method holds promise as a viable alternative for monitoring THMs concentrations in tap water, thereby contributing to the improvement of water quality management strategies.

Design and analysis of a multi-DOF compliant gait rehabilitation robot

Robot-assisted rehabilitation is a rapidly advancing field whereby robotic devices are used for the treatment of disabilities. Mechanism design and actuation of these devices play important roles in the treatment and the non-ergonomic designs may even increase the cardiorespiratory load and cause discomfort leading to further gait disorders. This article identifies crucial design and actuation aspects of gait rehabilitation robots and presents the first intrinsically compliant gait robot design with three actuated and five passive degrees of freedom, alleviating the prevalent issues. The gait robot joints remain aligned with the human anatomical joints during the swing and the stance phases due to the use of special bushes and dampers. The intrinsically compliant actuators on the new gait robot make it safe to work with human subjects. Increased degrees of freedom allow for natural walking dynamics instead of canceling or constraining them. The robot firmware, including the kinematic modeling, is designed using multiple adaptive neuro-fuzzy inference system to save on the computational time of the robot controller. The new robot design is finally validated for its intended use in gait rehabilitation, by employing a fuzzy logic controller and evaluating the position, velocity, and acceleration profiles of its joints.

INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS

This paper presents a Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS)-based method for regulating the handling force of a common object. The foundation of this method is the prediction of the inverse dynamics of a cooperative robotic system made up of two 3-DOF robotic manipulators. Considering the no slip in contact between the tool and the object, an object is moved. to create and feed the MANFIS database, the inverse kinematics and dynamic equations of motion for the closed chain of motion for both arms are established in Matlab. Results from a SimMechanic simulation are given to demonstrate how well the suggested ANFIS controller works. Several manipulated object movements covering the shared workspace of the two manipulator arms are used to test the proposed control strategy.

Comparison of the Estimation Ability of the Tensile Index of Paper Impregnated by UF-Modified Starch Adhesive Using ANFIS and MLR

The purpose of the present study is to offer an optimal model to predict the tensile index of the paper being consumed to make veneer impregnated with different weight ratios of modified starch (from 3.18 to 36.8%) to urea formaldehyde resin (WR) containing different formaldehyde to urea molar ratios (MR, from 1.16:1 to 2.84:1) enriched by different contents of silicon nano-oxide (NC, from 0 to 4%) using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) and compare the precision of these two models to estimate the response being examined (tensile index). Fourier-transform infrared spectroscopy (FTIR) and transmittance electron microscopy (TEM) were also used to analyze the results. The results of studying the adhesive structure using FTIR analysis showed that as the WR increased to the maximum level and MR increased to the average level (3%), more ether and methylene linkage forms due to cross-linking. TEM analysis also indicated that if an average level of silicon nano-oxide is applied, there will be more cross-linking due to the more uniform distribution and suitable interactions between the adhesive and nanoparticles. The modeling results showed that the ANFIS model estimates have been closer to the actual values compared to the MLR model. It can be concluded that the model offered by ANFIS has a higher potential to predict the tensile index of the paper impregnated with the combined adhesive of UF resin and modified starch. However, the MLR model could not offer a good estimate to predict the response. According to the preferred approach to predict the most effective property of resin coated paper, modelling would be useful to the research community and the results are beneficial in industrial applications without spending more cost and time.

MACHINABILITY INVESTIGATION OF HIGH-STRENGTH 7068 ALUMINUM ALLOY: A POTENTIAL STUDY THROUGH EXPERIMENTATION, SPOTTED HYENA OPTIMIZATION AND ADVANCED MODELING APPROACHES

This research emphasizes the machinability investigation on CNC turning of 7068 aluminum alloys. CVD-coated carbide tool was implemented for the [Formula: see text] full-factorial-based turning experiments in dry conditions. Machinability study includes the assessment of flank wear, cutting tool vibration, surface roughness, cutting temperature, chip reduction coefficient, and chip morphology. The selected tool performed well as very low wear (0.030–0.045[Formula: see text]mm) and low surface roughness (0.28–1.14[Formula: see text][Formula: see text]m) were found. All the input variables have significant impact on the flank wear, cutting tool vibration, cutting temperature, and chip reduction coefficient while for surface roughness, the effects of cutting speed and feed were significant at the 95% confidence level. Further, a novel optimization tool namely the spotted hyena optimizer (SHO) algorithm was utilized to get the optimal levels of input variables. Additionally, two different modeling tools namely multiple adaptive neuro-fuzzy inference system (MANFIS) and radial basis function neural network (RBFNN) were utilized for formulating the cutting response models. Further, the average of the absolute error was estimated for each model and compared. The MANFIS modeling tool exhibited a more close prediction of outputs as compared to RBFNN, as the obtained average absolute error for each response was lower with MANFIS.

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

Multi-objective optimization technique for trajectory planning of multi-humanoid robots in cluttered terrain

Humanoid robots hold a decent advantage over wheeled robots because of their ability to mimic human exile. The presented paper proposes a novel strategy for trajectory planning in a cluttered terrain using the hybridized controller modeled on the basis of modified MANFIS (multiple adaptive neuro-fuzzy inference system) and MOSFO (multi-objective sunflower optimization) techniques. The controller works in a two-step mechanism. The input parameters, i.e., obstacle distances and target direction, are first fed to the MANFIS controller, which generates a steering angle in both directions of an obstacle to dodge it. The intermediate steering angles are obtained based on the training model. The final steering angle to avoid obstacles is selected based on the direction of the target and additional obstacles in the path. It is further works as input for the MOSFO technique, which provides the ultimate steering angle. Using the proposed technique, various simulations are carried out in the WEBOT simulator, which shows a deviation under 5% when the results are validated in real-time experiments, revealing the technique to be robust. To resolve the complication of providing preference to the robot during deadlock condition in multi-humanoids system, the dining philosopher controller is implemented. The efficiency of the proposed technique is examined through the comparisons with the default controller of NAO based on toques produces at various joints that present an average improvement of 6.12%, 7.05% and 15.04% in ankle, knee and hip, respectively. It is further compared against the existed navigational strategy in multiple robot systems that also displays an acceptable improvement in travel length. In comparison in reference to the existing controller, the proposed technique emerges to be a clear winner by portraying its superiority.

A control structure for ambidextrous robot arm based on Multiple Adaptive Neuro‐Fuzzy Inference System

This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro-fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability.

Application of Multiple Linear Regression Models and Adaptive Neuro-Fuzzy Inference System Models to estimate the Compressive Strength of Concrete

The most widely used composite construction material is concrete. Compressive strength is an important monitoring parameter for quality assurance at construction site. In this paper,two different models of Multiple Linear Regression ( MLR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are developed to predict the 28 day compressive strength of concrete using the experimental datasets available . These two models are compared . Sensitivity Analysis (SA) is carried out for two different sets of parameters. ANFIS models predict more effectively as their coefficients of multiple determination(R Square) are higher than those of MLR models. This shows that the nonlinear correlation among input variables is better represented in ANFIS models than in MLR models .Goal 12 of United Nations Sustainable Development deals with the sustainable consumption and production patterns and it is taken into account as environmentally degrading materials( flyash and blast furnace slag ) are used.

Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

PurposeThe objective of this paper is to develop a fast modelling technique for predicting magneto-rheological fluid damper behaviour under impact loading applications.Design/methodology/approachThe adaptive neuro-fuzzy inference system (ANFIS) technique was adopted to predict the behaviour of a magneto-rheological fluid (MRF) damper through experimental characterisation data. In this study, an MRF damper manufactured by Lord Corporation was used for characterisation using an impact pendulum test rig. The experimental characterisation was carried out with various impact energies and constant input currents applied to the MRF damper.FindingsThis research provided a fast modelling technique with relatively less error in predicting MRF damper behaviour for the development of control strategies. Accordingly, the ANFIS model was able to predict MRF damper behaviour under impact loading and showed better performance than the modified Bouc–Wen model.Research limitations/implicationsThis study only focused on modelling technique for a single type of MRF damper used for impact loading applications. It is possible for other applications, such as cyclic loading, random loadings and system identification, to be studied in future experiments.Original/ValueFuture researchers could apply the ANFIS model as an actuator model for the development of control strategies and analyse the control performance. The model also can be replicated in other industries with minor modifications to suit different needs.

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

PurposeThis paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.Design/methodology/approachThe three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.FindingsGraphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.Originality/valueThis paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

Modelling and characterisation of a magneto-rheological elastomer isolator device under impact loadings using interpolated multiple adaptive neuro fuzzy inference system structure

This paper presents the modelling and characterisation of a magneto-rheological elastomer isolator device (MREID) under impact loading using the adaptive neuro-fuzzy inference system (ANFIS) technique. The characterisation of an MREID under impact loading was performed using an impact pendulum test rig, and the data obtained from the experimental work was processed in form of force-velocity and force-displacement characteristics. In order to predict MREID behaviour in simulation analysis, multiple ANFIS models were proposed. A single ANFIS model represented a single kinetic energy produced by the impact mass used in experimental work. The experimental data was then used to train the ANFIS in predicting MREID behaviour and validating its performance. For verification, the prediction model, a parametric model (namely, the modified Bouc-Wen model) was developed, and the models were compared. The proposed interpolated multiple ANFIS model predicted the behaviour of the MREID with a high level of accuracy. The proposed model produced a better prediction than the modified Bouc-Wen model.

Weld bead graphical prediction of cold metal transfer weldment using ANFIS and MRA model on Matlab platform

A difficult task for the transport sector is to make its assemblies lighter and perform more efficiently. Use of aluminum and its alloys has increased extensively in this sector because of reduction in weight of the vehicles and resulting energy savings. High thermal conductivity and thermal expansion pose difficulty in welding of these alloys. Cold metal transfer (CMT), a low heat input welding process, is the best choice for welding of these alloys. However, controlling the welding input parameters is highly necessary to obtain defect-free and high strength welded joints. In the present study, an attempt is made to develop a Matlab software-based application by two approaches, such as multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), for predicting the complete weld bead shape (graphical representation) of AA5052 using the CMT welding process. The data inputs used for both approaches are welding current (A) and welding speed (mm/min), respectively. A graphical interface is built to help the user to choose welding input parameters and obtain directly a representation of the weld bead profile in graphical form. In addition, the output response shows the complete weld bead shape, which is defined by the X and Y coordinates of the various points in the weld bead profile. The results are validated with randomized tests against the weld bead shape predicted by Matlab. Comparatively, ANFIS is the more effective method for predicting the weld bead profile and shows better agreement with the experimental profile than MRA. Further, the reliability and stability of the ANFIS model were determined from the mean absolute error percentage, root mean square error values, and linear R2fit model, confirming that the ANFIS-based prediction is in better agreement with the experimental values than MRA.

Identification of novel promoters and genetic control elements derived from Chinese Hamster Ovary cells

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

PurposeThe purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage.Design/methodology/approachThis paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model.FindingsThe result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data.Research limitations/implicationsBecause the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied.Originality/valueThe study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.

MANFIS–GA Heat Transfer Analysis and Optimization of Fins with Elliptical Perforation

In the present work, numerical heat transfer analysis of fins with elliptic perforations is performed and a soft computing-based optimization method is proposed for its design. The computational analysis is performed by solving coupled heat and flow transport equations after validating the numerical model with an existing experimental result. A parametric study is done based on the numerical model to examine the effects of fin spacing, fin height, fin perforation major axis, minor axis lengths and the number of perforations. The analyzed model results in an increased heat transfer rate with volume reduction of up to 72% when compared to a rectangular solid fin. But the best heat transfer rate and volume reduction are not achieved for the same set of fin parameters. A multiple response optimization technique needed to arrive at a fin configuration that provides considerable reduction in weight along with an increase in heat transfer is proposed. It is difficult to obtain reasonable approximations using mathematical regressions for such multiple response optimizations as the system response is highly nonlinear. An attempt is made to use multiple output adaptive neuro-fuzzy inference system coupled with genetic algorithm to optimize the parameters of the fins with elliptical perforation considering increased heat transfer and weight reduction. The proposed new optimization algorithm is found to be more effective in determining the optimal parameters when compared to existing regression and soft computing methods for optimization.