Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods

2010 ◽  
Vol 69 (2) ◽  
pp. 275-286 ◽  
Author(s):  
Ersin Kolay ◽  
Kamil Kayabali ◽  
Yuksel Tasdemir
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Slake Durability Index ◽  
Slake Durability ◽  
Fuzzy Methods ◽  
Neuro Fuzzy ◽  
Durability Index ◽  
Artificial Neural

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

2013 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Golam Kabir ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Business Environment ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Artificial Neural

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.

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