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Adaptive Neuro-fuzzy Inference System (adaptive + neuro-fuzzy_inference_system)

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Engineering	87%

Selected Abstracts

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

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 8 2006
Kerim Guney
Abstract 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 [source]

Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systems

EXPERT SYSTEMS, Issue 4 2010
Elif Derya Übeyli
Abstract: A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases. [source]

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

EXPERT SYSTEMS, Issue 2 2007
Necaattin Bari
Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease. [source]

Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip line

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2008
Celal Yildiz
Abstract This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191,1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322 [source]

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

Neuro-fuzzy structural classification of proteins for improved protein secondary structure prediction

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 8 2003
Joachim A. Hering
Abstract Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neuro-fuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91% for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins. [source]