scholarly journals Prediction of Reponses in a Sustainable Dry Turning Operation: A Comparative Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shibaprasad Bhattacharya ◽  
Partha Protim Das ◽  
Prasenjit Chatterjee ◽  
Shankar Chakraborty
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Prediction Models ◽  
Machining Parameters ◽  
Anfis Model ◽  
Inference System ◽  
Turning Operation ◽  
Dry Turning ◽  
Statistical Measures ◽  
Cylindrical Workpiece

In a turning operation, involving removal of material from the outer diameter of a rotating cylindrical workpiece using a single-point cutting tool, there exist complex relationships between various cutting parameters and responses. In this paper, a turning operation under dry environment is considered with cutting speed, feed rate, and depth of cut as the input parameters, as well as material removal rate, average surface roughness, and cutting force as the responses. Dry turning operation reduces energy consumption and machining cost, thus eventually resulting in sustainable machining. For the considered process, the corresponding response values are envisaged using four prediction models, that is, multivariate regression analysis, fuzzy logic, artificial neural network, and adaptive neurofuzzy inference system (ANFIS), and their prediction performance is contrasted using five statistical metrics, that is, root mean squared percent error, mean absolute percentage error, root mean squared log error, correlation coefficient, and root relative squared error. It is noticed that ANFIS model consisting of the advantages features of both fuzzy logic and neural network outperforms the other prediction models with respect to the computed values of the considered statistical measures. Based on their acceptable values, it can be propounded that the ANFIS model can be effectively employed for prediction of process responses while treating different machining parameters as the input variables.

Theoretical Investigation of a Structure for Active Vibration Control with Fuzzy Logic Approach

2014 ◽  
Vol 598 ◽  
pp. 529-533
Author(s):  
Erdi Gülbahçe ◽  
Mehmet Çelik ◽  
Mustafa Tinkir
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Modal Analysis ◽  
Prediction Models ◽  
Block Diagram ◽  
Active Vibration Control ◽  
Control Block ◽  
Inference System ◽  
Active Vibration

The main purpose of this study is to prepare mathematical model for active vibration control of a structure. This paper presents the numerical and experimental modal analysis of aluminum cantilever beam in order to investigate the dynamic characteristics of the structure. The results will be used for active vibration control of structure’s experimental setup. Experimental natural frequencies are obtained and compared to verify the proposed numerical model by using modal analysis results. MATLAB System Identification Toolbox and ANSYS harmonic response function are used together to estimate beam’s equations of motion which include its amplitude, frequency and phase angle values. Moreover, the mathematical model of beam is simulated in MATLAB/Simulink software to determine the dynamic behavior of the proposed system. Furthermore, another prediction model approach with multiple input and single output is used to find the realistic behavior of beam via an adaptive neural-network-based fuzzy logic inference system, in addition, impulse responses of the proposed models are compared and the control block diagram for active vibration control is implemented. As a first iteration, PID type controller is designed to suppress vibrations against the disturbance input. The results of modal analysis, the prediction models, controlled and uncontrolled system responses are presented in graphics and tables for obtaining a sample numerical active vibration control.

scholarly journals Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2771 ◽  
Author(s):  
Abbas Mardani ◽  
Dalia Streimikiene ◽  
Mehrbakhsh Nilashi ◽  
Daniel Arias Aranda ◽  
Nanthakumar Loganathan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Fuzzy Inference System ◽  
Global Economy ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

2016 ◽  
Vol 12 (1) ◽  
pp. 47-62 ◽  
Author(s):  
Asogbon Mojisola Grace ◽  
Samuel Oluwarotimi Williams
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Risk Level ◽  
Inference System ◽  
Wrong Decision

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

Fuzzy Modeling and Analysis on the Turning Parameters for Machining Force and Specific Cutting Pressure in CFRP Composites

2013 ◽  
Vol 766 ◽  
pp. 77-97
Author(s):  
T. Rajasekaran ◽  
V.N. Gaitonde ◽  
J. Paulo Davim
Keyword(s):  
Fuzzy Logic ◽  
Composite Materials ◽  
Prediction Models ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Sem Images ◽  
Machining Force ◽  
Soft Computing Techniques

Analysis of the cutting force and specific cutting pressure play vital roles in machining of the composite materials. The present experimental work describes the modeling of machining parameters using one of the soft computing techniques i.e. fuzzy logic for machining force and specific cutting pressure. The basic idea is to machine the carbon fiber reinforced plastic (CFRP) composite materials and measuring the cutting forces and then determining the machining force and specific cutting pressure. 27 experiments based on Taguchis L27 orthogonal array were carried out involving three machining parameters, namely, cutting speed, feed and depth of cut, each defined at three levels. Subsequently the prediction models were developed using three different fuzzy logic membership functions, namely, triangular, trapezoidal and bell shape. It is found that the predicted values of proposed responses such as machining force and specific cutting pressure are very close to the experimental values within the chosen ranges of the process parameters. The statistical analysis using ANOVA on machining parameters are also presented and discussed. The machined surface analyzed through SEM images revealed the damages encountered during turning process.

scholarly journals Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms

2021 ◽  
Vol 6 (9) ◽  
pp. 129
Author(s):  
T. Pradeep ◽  
Abidhan Bardhan ◽  
Avijit Burman ◽  
Pijush Samui
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Optimization Algorithms ◽  
Grey Wolf Optimizer ◽  
Statistical Parameters ◽  
Unconfined Compression Test ◽  
Rock Stratum ◽  
Inference System ◽  
Prediction Approach

The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the significant works for the assessment of the failure of rock material. The purpose of this paper is to investigate the development of a new strain prediction approach in rock samples utilizing deep neural network (DNN) and hybrid ANFIS (adaptive neuro-fuzzy inference system) models. Four optimization algorithms, namely particle swarm optimization (PSO), Fireflies algorithm (FF), genetic algorithm (GA), and grey wolf optimizer (GWO), were used to optimize the learning parameters of ANFIS and ANFIS-PSO, ANFIS-FF, ANFIS-GA, and ANFIS-GWO were constructed. For this purpose, the necessary datasets were obtained from an experimental setup of an unconfined compression test of rocks in lateral and longitudinal directions. Various statistical parameters were used to investigate the accuracy of the proposed prediction models. In addition, rank analysis was performed to select the most robust model for accurate rock sample prediction. Based on the experimental results, the constructed DNN is very potential to be a new alternative to assist engineers to estimate the rock strain in the design phase of many engineering projects.

Fuzzy Logic and Intelligence System

2000 ◽  
Vol 4 (5) ◽  
pp. 319-320
Author(s):  
Hyung Lee-Kwang ◽  
◽  
Ju-Jang Lee
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Rough Set ◽  
Intelligent Systems ◽  
Fuzzy Logic Controller ◽  
Fuzzy Rule ◽  
Wavelet Neural Network ◽  
Classification Method ◽  
Inference System

These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.

Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic

2011 ◽  
Vol 110-116 ◽  
pp. 2976-2982 ◽  
Author(s):  
Sina Eskandari ◽  
Behrooz Arezoo ◽  
Amir Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Previous Method ◽  
Data Set ◽  
Inference System ◽  
Thermal Errors ◽  
Squared Error

Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.

Modeling of TiN Coating Thickness Using ANFIS

2014 ◽  
Vol 575 ◽  
pp. 3-7 ◽  
Author(s):  
Abdul Syukor Mohamad Jaya ◽  
A. Samad Hasan Basari ◽  
Sazalinsyah Razali ◽  
Muhd Razali Muhamad ◽  
Md Nizam Abd Rahman
Keyword(s):  
Experimental Data ◽  
Coating Thickness ◽  
Prediction Accuracy ◽  
Physical Vapor Deposition ◽  
Prediction Models ◽  
Residual Error ◽  
Confirmatory Test ◽  
Anfis Model ◽  
Inference System ◽  
Tin Coatings

In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

scholarly journals Gait Recognition Analysis for Human Identification Analysis- A Hybrid Deep Learning Process

2021 ◽  
Author(s):  
Mathivanan B ◽  
Perumal P
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Median Filter ◽  
Gait Recognition ◽  
Human Gait ◽  
Scale Invariant ◽  
Inference System ◽  
Statistical Measures ◽  
Hu Moments

Abstract Gait is an individual biometric behavior which can be detected based on distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, Advanced Deep Belief Neural Network with Black Widow Optimization (ADBNN-BWO) Algorithm is developed to identify the human emotions by human walking style images. This proposed methodology is working based on four stages like pre-processing, feature extraction, feature selection and classification. For the pre-processing, contrast enhancement median filter is used and Hu Moments, GLCM, Fast Scale-invariant feature transform (F-SIFT), in addition skeleton features are used for the feature extraction. To extract the features efficiently, the feature extraction algorithm can be often very essential calculation. After that, feature selection is performed. Then the classification process is done by utilizing the proposed ADBNN-BWO Algorithm. Based on the proposed method, the human gait recognition is achieved which utilized to identify the emotions from the walking style. The proposed method is validated by using the open source gait databases. The proposed method is implemented in MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively.

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