Adaptive Network-based Fuzzy Inference System System Research Articles

Tool life and wear prediction of HSS and PVD material using ANFIS system

This paper proposes tool wear prediction of the cutting material using High Speed Steel (HSS) and Physical Vapor Deposition (PVD) coating using Adaptive Network-based fuzzy inference system (ANFIS) system. Even though machines have been studied and practiced for a long time, but quality and accuracy are still needed to improve at the factory level. Machinating data and cutting parameters are derived from the cutting tool manufacturing for recommendation and initial decision making. However, practical machining depends on the practitioners and specific cases of machining. Tool wear is a critical aspect for improvement of productivity. Unlike in the previous time, machinists were made decision by totally experiences whereas the modern technology provide usefully mathematical simulation. The advanced method is combining people’s experiences and computer system by heuristic rule based which is known as ANFIS system. Input membership functions are defined and fed into the MATLAB toolbox. The rules are captured by shop floor experts and created in the fuzzy inference system. It is found from the simulation that HSS tool effects extremely on the high cutting speed whereas the PVD is risky to occur the wear at the cutting speed and feed rate. It can conclude that the PVD can be used by the medium to high cutting speed whereas the HSS performs good at the low to medium cutting speed. However, the tool life prediction is limited to direct prediction, but it can be calculated by the Taylor tool life equation.

Adaptive-network-based Fuzzy Inference (anfis) Modelling of Particle Image Velocimetry (piv) Measurements in Stirred Tank Reactors

Mixing represents an energy-intensive unit operation which can significantly influence the performance of the different industrial processes. Efficient mixing is necessary to reduce spatial inhomogeneities within reactors and bioreactors. Particularly, in bioreactors, shear stress on microorganisms and physicochemical gradients might affect the physiological state of the biotic phase and, hence, decrease bioreaction yields.The present work presents an innovative machine-learning modelling approach which uses the adaptive-network-based fuzzy inference system (ANFIS) on experimentally velocity fields data collected through Particle Image Velocimetry (PIV) in STR under different operating conditions. The calibration and optimization of the ANFIS system are performed by dividing the PIV data into two subsets: training and validation data. Then, a sensitivity analysis is carried out varying the percentage of training data and certain features of the membership functions (number and type). The fitness of the produced models was scored by means of the fuzzy Goodness Index (GI), which combines the correlation coefficient (R2), index of agreement (IA) and relative root mean square error (RRMSE).

The Optimization Design of a Novel Slotted Microstrip Patch Antenna with Multi-Bands Using Adaptive Network-Based Fuzzy Inference System

This paper attempts to apply an adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a single-layer single-patch microstrip rectangular patch antenna with two equal size slots which are placed on the patch in the form of parallel to resonance edges. The resonant frequency is calculated as the position of the slots is shifted from the right endpoint to the left endpoint on the patch between −4.2 mm ≤ Xslot ≤ 4.2 mm with the steps of 0.1 mm. The designed antenna is proposed for downlink of X band satellite, broadcasting satellite service, fixed-satellite service uplink, satellite (Earth-to-space), radio navigation, mobile-satellite (Earth-to-space), and KU band which can be achieved at the resonant frequencies of 7.2 GHz, 12.2 GHz, 14.6 GHz, 17.5 GHz and 19.3 GHz. Next, High Frequency Electromagnetic Field Simulation software (ANSYS HFSS) results for the prototype microstrip antenna are compared with the values obtained through ANFIS system. It can be concluded that the adaptive network-based fuzzy inference system in such designs can be conveniently used due to fuzzy system’s high approximation capability and much faster convergence rate. The best results for our ANFIS system can be obtained if Gaussian membership is used which leads to the mean absolute error of 1.4653.

Compensate Voltage Sags in Series Transformers of Dynamic Voltage Restorers for Preventing Saturation

This paper proposes a procedure for preventing saturation in series transformers from Dynamic Voltage Restorer (DVR) systems. Power quality has been an issue that is becoming increasingly pivotal in modern industrial and commercial applications. Voltage disturbances especially the voltage sag and swell are the most common power quality problems due to increased use of a large numbers of sophisticated and sensitive electronic equipment in industrial systems. The method consists in correcting the voltage which is injected through the transformers into the power system to compensate voltage sags. To overcome this problem, custom power devices are used. One of the devices is the Dynamic Voltage Restorer (DVR), which is the most efficient and effective modern custom power device used in power distribution networks. It is a series connected power electronic based device that can quickly mitigate the voltage sags in the system and restore the load voltage to the pre-fault value. Moreover, the technique allows a certain level of sag compensation even when the estimated flux is expected to exceed the saturation limit. The voltage sag level and phase are computed through an adaptive Recursive Least Squares (RLS). In RLS Method we have to use 4-leg voltage source inverter and it can be slow process to compensate the voltage sag occur in the power system. In extension method, the Adaptive Network based Fuzzy Inference System (ANFIS) is used to reduces the voltage sag in the power system network. In these method, the 3-leg voltage source inverter as comparing to the RLS algorithm. The ANFIS system is faster and accurate to compensate voltage sags in power system. Total harmonics distortions is half of the deduced in the system.Keywords: Adaptive System Based Fuzzy Interface System (ANFIS), Dynamic Voltage Restorer (DVR), Recursive Least Squares (RLS), Saturation, Series Transformers, Voltage Sag

ANFIS based model for power loss estimation of metal oxide surge arrester

This paper proposes a new method to estimate power loss characteristics of metal oxide surge arresters. The method was used to compute surge arrester power loss curve based on adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Surge arrester operating history is an important factor that bears influence on its power loss. Degradation was, in this paper, introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature and degradation factor were considered as inputs in ANFIS system and ANN models to obtain accurate power loss which is a very important factor in surge arrester thermal stability. Degrading effect was undertaken by measurement voltage and current in varistors degraded by utilization in network. The results of the two artificial models were compared. Results show that ANFIS was more accurate than ANN. This study shows that the power loss characteristics of surge arrester are to a great extent accurately predictable using proposed artificial model.

An Application of ANFIS to Predict the Hot Flow Behavior of 6063 Aluminum Alloy

In order to determine the optimum hot-forming processing parameters for 6063 aluminum alloy, the compressive deformation behavior of 6063 aluminum alloy was investigated at the temperatures from 300 to 500 °C and strain rates from 0.5 to 50 s−1 on a Gleeble-1500 Thermal Simulator. Based on the compression experimental data, a novel adaptive network-based fuzzy inference system (ANFIS) model is developed to predict the flow behavior of 6063 aluminum alloy. In the ANFIS system, the inputs of the ANFIS are the strain, the strain rate and the temperature, whereas the flow stress is the output. The effects of strain, strain rate, and temperature on the flow behavior of 6063 aluminum alloy have been studied by comparing the experimental and the predicted results using the developed ANFIS model. The results show that predicted values using the developed model are in good agreement with the experimental data, which demonstrates the reliability of the developed ANFIS model.

Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network-based Fuzzy Inference System (ANFIS) as a system identifier. The proposed hybrid learning algorithm is based on the particle swarm optimization (PSO) for training the antecedent part and the extended Kalman filter (EKF) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. Comparison results of the proposed approach, PSO algorithm for training the antecedent part and recursive least squares (RLSs) or EKF algorithm for training the conclusion part, with the other classical approaches such as, gradient descent, resilient propagation, quick propagation, Levenberg–Marquardt for training the antecedent part and RLSs algorithm for training the conclusion part are provided. Moreover, it is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model. Stability constraints are obtained and different simulation results are given to validate the results. Also, the stability of Levenberg–Marquardt algorithms for ANFIS training is analyzed.

New speech/music discrimination approach based on warping transformation and ANFIS

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents an effective approach based on an Adaptive Network-Based Fuzzy Inference System (ANFIS) for the classification stage required in a speech/music discrimination system. A new simple feature, called Warped LPC-based Spectral Centroid (WLPC-SC), is also proposed. Comparison between WLPC-SC and the classical features proposed in the literature for audio classification is performed, aiming to assess the good discriminatory power of the proposed feature. The length of the vector for describing the proposed psychoacoustic-based feature is reduced to a few statistical values (mean, variance and skewness), which are then transformed to a new feature space by LDA, with the aim of increasing the classification accuracy percentage. The classification task is performed applying ANFIS to the features in the transformed space. To evaluate the performance of the ANFIS system for speech/music discrimination, comparison to other commonly used classifiers is reported. The classification results for different types of music and speech signals show the good discriminating power of the proposed approach.