Parameters Of Adaptive Neuro-fuzzy Inference System Research Articles

Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot

ABSTRACT We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-tracking problems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve the tracking performance based on its trained model. The ANFIS parameters are tuned using both particle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results of two different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposed AFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFIS performance is evaluated and compared with those from other controllers using the average tracking error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach with a rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion

Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots’ global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi–Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.

A hybrid GA-ANFIS and F-Race tuned harmony search algorithm for Multi-Response optimization of Non-Traditional Machining process

The present study focuses on development of prediction models with respect to various cut quality characteristics such as material removal rate, kerf taper and surface roughness for a well-known non-traditional machining process namely abrasive aqua jet cutting (AAJC) of natural fibre composite laminates through combined taguchi-genetic algorithm (TGA) and adaptive neuro fuzzy inference system (ANFIS). The AAJC experiments are conducted based on box-behnken design methodology by considering jet pressure, stand-off distance, traverse speed and wt% of nano clay inclusion in composites as input parameters. The ANFIS parameters are optimized using a hybrid taguchi-genetic training algorithm. The statistical results of hybrid TGA-ANFIS models shows that they are outperformed in prediction of AAJC parameters when compared with the results of multiple-linear regression models. Further, the optimization of AAJC parameters is carried out using a trained ANFIS network and the F-race tuned harmony search algorithm (HSA). The superlative responses such as MRR of 76.9 g/min, KT of 2.23° and Ra of 3.17 µm are forecasted at the optimum cutting conditions such as jet pressure of 303.08 MPa, stand-off distance of 2.16 mm, traverse speed of 375.64 mm/min, and nano clay wt% of 1.27, respectively. The experimental results show that the error between predicted and actual results are lower than 6%, indicating the feasibility of adopting the proposed F-race parametric tuned HSA in optimization of AAJC process.

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete.

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase.

Evaluation of Optimal Substitute Ratio of Fly Ash Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Study

Fly ash is widely used for replacing partial cement and producing high-performance concrete. The concrete production company is interested in the optimal substitute ratio of fly ash. This study presents a general procedure for evaluating the optimal substitute ratio of fly ash. First, the compressive strength of fly ash blended concrete is evaluated based on adaptive neuro-fuzzy inference system (ANFIS). The water-to-binder ratio, fly ash replacement ratio, and ages are used as input parameters of ANFIS. Strength is the output parameter of ANFIS. Second, sensitivity analysis is performed using ANFIS. The development of relative strength of fly ash blended concrete is calculated considering water-to-binder ratio, fly ash replacement ratio, and ages. The analysis results show that the optimal replacement fly ash is dependent on water-to-binder ratio of concrete. As thewater-to-binder ratio decreases from 0.5 to 0.3, the optimal substitute ratio of fly ash increases from 15% to 30%.

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.

Estimation of wind energy power using different artificial intelligence techniques and empirical equations

ABSTRACT The understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems.

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

AbstractA novel intelligent‐mechanistic model was developed to understand the behavior of multiphase chemical reactors. Computational fluid dynamics (CFD) and an intelligent algorithm were combined to predict different levels of 3D cylindrical bubble‐column reactors. An adaptive neuro‐fuzzy inference system (ANFIS) was used as the intelligence algorithm, and different ANFIS parameters were evaluated. With about one third of the training data the method can predict the overall behavior of the gas fraction in the reactor. A number of rules significantly influence the accuracy of the ANFIS method. After finding appropriate parameters, the method is applied for prediction of points which are not simulated with CFD, representing ANFIS mesh refinement. Also, bubble‐column reactors without training of exact values of measured data or numerical results can be predicted. Main advantages are time savings and reduction of computational expenses.

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.

Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

Abstract In this paper, a new method is presented for the training of the Adaptive Neuro-Fuzzy Inference System (ANFIS). In this work, it is ensured that the best model is created by optimising the premise and consequent parameters of ANFIS by using Simulating Annealing (SA) based on an iterative algorithm. The proposed method was applied to dynamic system identification problems. The simulation results of the proposed method are compared with the Genetic algorithm (GA), Backpropagation (BP) algorithm and different methods from the literature. At the end of this study it was found that the optimisation of ANFIS parameters is more successful by using SA than by GA, BP and the other methods.

An effective pose invariant face recognition system with the aid of ABC optimized ANFIS

Utilizing a stocked up database of faces, Face recognition recognizes or verifies one or else more individuals in the specified still or else video images of a scene. This is owing to its abundant vital applications in security, human–computer interactions, authentication, and surveillance. A method intended for efficacy face recognition system on the video sequence is splashed by this paper. Deducting its intricacies is the salient cause of this paper. In the proposed posture invariant face recognition technique, first database video clip is separated into variant frames. Preprocessing is executed for every frame for removing the noise utilizing Gaussian filtering. The Face is identified from the preprocessed image by utilizing Viola–Jones algorithm. Then effective features are excerpted from detected face. Next, the excerpted feature value will present as input to train for adaptive neuro-fuzzy inference system (ANFIS) classifier. ANFIS parameters are optimized through artificial bee colony ABC algorithm to derive prompt high acknowledgment throughout the training. Likely the query image’s features are employed for verifying the suggested recognition system performance. The technique will be achieved on the working platform in MATLAB in addition the outcome will be examined and matched with the prevailing techniques for elucidating the suggested video face recognition method’s performance.

Optimization of Fuzzy Neural Networks using Mine Blast Algorithm for Classification Problem

The integration of Fuzzy Neural Networks (FNNs) with optimization techniques has not only solved the issues “black box” in Artificial Neural Networks (ANNs) but also has been effective in a wide variety of real-world applications. Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rules optimization methods to perform efficiently when the number of inputs increases. ANFIS accuracy depends on the parameters it is trained with and the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm. Many researchers have trained ANFIS parameters using metaheuristic, however, very few have considered optimizing the ANFIS rule-base. We propose an effective technique for optimizing ANFIS rule-base and training the network parameters using newly Accelerated modified MBA (AMBA) to convergence the speed during exploitation phase. The AMBA optimized ANFIS was tested on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The AMBA optimized ANFIS has also been employed to model real datasets. The performance of the proposed AMBA optimized ANFIS model was compared with the ones optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed AMBA optimized ANFIS achieved better accuracy with optimized rule-set in less number of function evaluations. Moreover, the results also indicate that AMBA converges earlier than its other counterparts.

Optimal design of convertible static compensator supplementary damping controller to avoid wide area uncontrolled islanding

This study presents a new design of a wide area damping controller for the convertible static compensator (CSC) to limit some major blackouts due to asynchronous oscillations. The adaptive neuro-fuzzy inference system (ANFIS) approach is adopted in the dual heuristic programming method to have an optimal response at different disturbances. The proposed ANFIS parameters are optimised by using the multi-swarm particle swarm optimisation method. The CSC dynamic performance in both of unified power flow controller and interline power flow controller configurations is investigated to increase the damping of inter-area oscillations and to prevent the uncontrolled network islanding. The Iranian power grid and New England power system are selected to install CSC and to apply the designed damping controller. It is shown that the considerable number of wide area blackouts could be avoided by CSC application equipped with the proposed damping controller. In other scenarios, which the controlled islanding is an unavoidable task, the boundaries of islands are determined adaptively considering the changes of generators coherency and the loadings of lines. Simulation results verify that the load shedding amounts are decreased and the stability margins of islands are increased. The uncontrolled islanding condition is also delayed significantly.

A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm

Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, such as cardiac signal (electrocardiogram), signals generated by eye movement/eye blinks (electrooculogram) and muscular artefact signal (electromyogram), called artefacts. Optimisation is an important tool for solving many real-world problems. In the proposed work, artefact removal, based on the adaptive neuro-fuzzy inference system (ANFIS) is employed, by optimising the parameters of ANFIS. Artificial Immune System (AIS) algorithm is used to optimise the parameters of ANFIS (ANFIS-AIS). Implementation results depict that ANFIS-AIS is effective in removing artefacts from EEG signal than ANFIS. Furthermore, in the proposed work, improved AIS (IAIS) is developed by including suitable selection processes in the AIS algorithm. The performance of the proposed method IAIS is compared with AIS and with genetic algorithm (GA). Measures such as signal-to-noise ratio, mean square error (MSE) value, correlation coefficient, power spectrum density plot and convergence time are used for analysing the performance of the proposed method. From the results, it is found that the IAIS algorithm converges faster than the AIS and performs better than the AIS and GA. Hence, IAIS tuned ANFIS (ANFIS-IAIS) is effective in removing artefacts from EEG signals.

ANFIS modeling of Electro-Hydraulic Actuator system through physical modeling and FCM gap statistic in initial FIS determination

The Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has shown the ability to model nonlinear system at high accuracy. The initial Fuzzy Inference System (FIS) is required before ANFIS parameters' training. The generation of initial FIS requires the number of inputs, number of clusters and cluster centers to be determined. Instead of determining the above parameters by heuristic approach, the number and variable of inputs of an Electro-Hydraulic Actuator (EHA) system are determined through mathematical derivation of the system. The number of clusters for each input is determined via fuzzy gap statistic, which applies Fuzzy C-means (FCM) instead of k-means of gap statistic in calculating the within cluster dispersion. FCM clustering also provides the center of each cluster identified. Information of the input variables and cluster numbers as well as cluster centers are used to obtain initial FIS. ANFIS hybrid training algorithm is used to train the initial FIS for parameters. The result shows that the model obtained using ANFIS approach can estimate the EHA's system response at high precision.

An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

Dynamic modelling and control of a twin-rotor system using adaptive neuro-fuzzy inference system techniques

This article presents an online non-linear dynamic modelling and control approach based on adaptive neuro-fuzzy inference system (ANFIS) for a twin-rotor multi-input multi-output system (TRMS), in the vertical plane motion. The TRMS can be considered as a flexible aerodynamic test rig that resembles the behaviour of a helicopter. The TRMS and similar manoeuvring systems are often subjected to random disturbances arising from various sources such as driving motors and external environmental sources. For such highly non-linear systems with varying operating conditions, adaptive control approaches are suitable tools to cope with plant uncertainties. An inverse-model control of the TRMS is developed using online ANFIS learning algorithm. The consequent and antecedent parameters of a Takagi–Sugeno fuzzy inference system are optimized online using recursive least squares and gradient descent algorithms, respectively. In order to reduce the computation complexity, the training process is minimized based on global system error tolerance. The optimal initialization of the ANFIS parameters is achieved through an off-line training process. The developed strategy is compared to other control laws in terms of tracking performance, disturbance rejection, and response to external noise. The obtained simulation results demonstrate the efficiency of the online inverse control scheme.

Adaptive neuro-fuzzy inference system for computing patch radius of circular microstrip antennas

A new method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented to calculate accurately the patch radius of circular microstrip antennas (MSAs). ANFIS combines the benefits of artificial neural networks (ANNs) and fuzzy inference systems (FISs) in a single model. A hybrid learning algorithm based on the least-squares method (LSM) and the backpropagation algorithm is used to identify the parameters of ANFIS. The results of ANFIS are in very good agreement with the experimental results reported elsewhere. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1606–1610, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21695

Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System

This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The input resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.