ANFIS Modeling Based on Full Factorial Design

Buragohain, Mrinal; Mahanta, Chitralekha

doi:10.1109/icit.2006.372435

Cited by 15 publications

(21 citation statements)

References 13 publications

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“…Fuzzy if-then rules that contain the parts premise and consequent of Sugeno type fuzzy inference system has been applied in the structure of ANFIS [15]. The assumption in this system is that the interference system has two inputs x and y and one output f. For a first order Sugeno fuzzy model the rule basis including two fuzzy if-then rules is as follows:…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…But in the backward pass, the derivative of the squared error with respect to each node output, named error signals, duplicate backward from the output layer to the input layer. In this pass, the premise parameters would be updated by the gradient descent algorithm [15][16].…”

Section: Learning Algorithmmentioning

confidence: 99%

“…self-organized maps (SOM) and adaptive resonance theory (ART) have been applied to fulfill the same objective in other studies [7,8]. The advantage with neural network is that it is capable of handling noisy 15 cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier.…”

Section: Introductionmentioning

confidence: 99%

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Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study

Atoon¹,

Azarbad²

2017

IJSIP

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: Learning Algorithmmentioning

confidence: 99%

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Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study

Atoon¹,

Azarbad²

2017

IJSIP

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“…These predicted results help making admission decisions more efficiently and improve quality of academic services [2]. Particularly, administrators can evaluate _______ the advantages from both methods [5,6]. There have been many neuro-fuzzy models namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Coactive Neuro-Fuzzy Inference System (CANFIS), Hierarchical Adaptive Neuro-Fuzzy Inference System (HANFIS), Multi Adaptive Neuro-Fuzzy Inference System (MANFIS) [7][8][9].…”

mentioning

confidence: 99%

A new proposal Classification method based on Fuzzy Association Rule Mining for Student Academic Performance Prediction

Giap¹

2017

PaM

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Predicting student academic performance (SAPP) is an important issue in modern education system. Proper prediction of student performance improves construction of education principle in universities and helps students select and pursue suitable occupation. The predictions approaching fuzzy association rules (FAR) give advantages in this circumtance because it give the clear data-driven rules for prediction outcome. Applying fuzzy concept brings the linguistic terms that is close to people thought over a quantitative dataset, however an efficient mining mechanism of FAR require a high computing effort normally. The existing FAR-based algorithms for SAPP often use Apriori-based method for extracting fuzzy association rules, therefor they generate a huge number of candidates of fuzzy frequent itemsets and many redundant rules. This paper presents a new proposal model of predictor using FAR to elevating prediction performance and avoids extraction of the fixed set of FAR before prediction progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzy frequent itemset mining, when a new requirement raised, the proposed algorithm mines directly in the tree structure for the best prediction result. The proposal model does not require to pre-determine the actecedent of prediction problem before the training phrase. It avoids searching for non-relative rules and prunes the conflict rules easily by using a new rule relatedness estimation.

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“…ANFIS consists of three components, such as the rule base containing the selection of fuzzy rules, a database which defines the membership function used in the rules and a reasoning mechanism to carry out inference procedure on the rules and given facts. ANFIS uses hybrid learning method (Buragohain and Mahanta, 2008) and combines gradient descent and least squares method to identify the consequent parameters that defines the coefficients of output parameters. The architecture of a typical ANFIS consists of five layers, which perform different actions in the ANFIS are detailed below.…”

Section: Prediction Of Emitted Radiation Of Heat Sink Using Mlra and mentioning

confidence: 99%

Prediction and comparison of emitted radiation from a flat plate heat sink using linear regression analysis with artificial neural network and ANFIS

Manivannan

Devi

Arumugam³

et al. 2011

IJIT

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This paper provides a comparison of multiple linear regression analysis with artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), for predicting the emitted radiations from a flat plate heat sink. HFSS simulations were designed using Ansoft version 12 for the L27 orthogonal array and optimised using Taguchi design of experiments method. The heat sink geometry factors considered for the L27 (six factors, three levels) design are length, width, fin height, base height, fin thickness and number of fins and the response studied is the emitted radiation from the heat sink. A meta model is developed based on a multiple linear regression method using HFSS simulations. Also, the results of L27 orthogonal array were used to train the artificial neural network and the ANFIS-based intelligent networks. The accuracy of results for the prediction of emitted radiations using multiple linear regressions, ANN and ANFIS were compared with HFSS simulations. From the results, it is found that the ANFIS outperforms ANN and regression models for the prediction of the emitted radiations from the heat sink.Keywords: emitted radiation; heat sink; multiple linear regression analysis; MLRA; artificial neural networks; ANN; adaptive neuro fuzzy inference system; ANFIS.Reference to this paper should be made as follows: Manivannan, S., Devi, S.P. Biographical notes: S. Manivannan is currently pursuing his research in AnnaUniversity in the area of thermal and EMI management of electronic packages. He has obtained his BE and ME from Madras University. He has published over seven papers in referred international journals.S. Prasanna Devi is pursuing her research in Anna University. She obtained her BE and ME from the Department of Computer Science from the same university. She has published over ten papers in refereed international journals in the area of operations research.

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ANFIS Modeling Based on Full Factorial Design

Cited by 15 publications

References 13 publications

Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study

Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study

A new proposal Classification method based on Fuzzy Association Rule Mining for Student Academic Performance Prediction

Prediction and comparison of emitted radiation from a flat plate heat sink using linear regression analysis with artificial neural network and ANFIS

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