scholarly journals Indonesia Scholarship Selection Model Using a Combination of Back-Propagation Neural Network and Fuzzy Inference System Approaches

2018 ◽  
Vol 11 (3) ◽  
pp. 79-90 ◽  
Author(s):  
Luther Latumakulita ◽  
◽  
Tsuyoshi Usagawa ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Selection Model ◽  
Inference System ◽  
System Approaches

scholarly journals ANFIS AND NEURAL NETWORK BASED FACIAL EXPRESSION RECOGNITION USING CURVELET FEATURES

2014 ◽  
pp. 255-261
Author(s):  
S.P. Khandait ◽  
R.C. Thool ◽  
P.D Khandait
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Curvelet Transform ◽  
Back Propagation Neural Network ◽  
Expression Recognition ◽  
Inference System ◽  
Promising Tool

Curvelet transform is a promising tool for multi-resolution analysis on images. This paper explains a new approach for facial expression recognition based on curvelet features extracted using curvelet transform. Curvelet transform is applied on the database images and curvelet coefficients are obtained for selected scale for image analysis. Facial curvelet features are compressed using singular value decomposition (SVD) approach. Back propagation neural network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as classifiers for classifying expressions into one of the seven categories like angry, disgust, fear, happy, neutral, sad and surprise. Experimentation is carried out on JAFFE database. The experimental results show that the novel approach is a better option for extracting feature values and classifying facial expressions.

The Study of System Model Based on Fuzzy Inference and Neural Network

2012 ◽  
Vol 197 ◽  
pp. 547-552
Author(s):  
Ming Ming Gao ◽  
Liang Shan
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Fuzzy Rules ◽  
System Model ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Anfis Method

For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.

COMPARATIVE PERFORMANCE FOR PREDICTIVE MODELLING IN MOTOR INSURANCE CLAIMS

2016 ◽  
Vol 78 (12-2) ◽  
Author(s):  
Zuriahati Mohd Yunos ◽  
Siti Mariyam Shamsuddin ◽  
Razana Alwee ◽  
Noriszura Ismail ◽  
Roselina Salleh@Sallehuddin
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Predictive Modelling ◽  
Back Propagation Neural Network ◽  
Insurance Claims ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Motor Insurance ◽  
Claim Frequency

The expected claim frequency and the expected claim severity are used in predictive modelling motor insurance claims. There are two categories of claims were considered, namely, third party property damage and own damage. Datasets from the year 2001 to 2003 are used to develop the predictive model. This paper proposes three different methods, namely, regression analysis, back propagation neural network and adaptive neuro fuzzy inference system to model claim frequency and claim severity as the two important elements in modelling the motor insurance claims. The experimental results showed that the back propagation neural network model produces more accurate as compared to regression analysis and adaptive neuro fuzzy inference system in predicting the claim frequency and claim severity. For both OD and TPPD claim, the results have shown the lowest MAPE with 0.2191 and 0.6515, and 0.2169 and 0.326, respectively.

scholarly journals ANALISA DAN PERBANDINGAN AKURASI MODEL PREDIKSI RENTET WAKTU ARUS LALU LINTAS JANGKA PENDEK

2015 ◽  
Vol 6 (3) ◽  
pp. 148
Author(s):  
Bambang Lareno
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy

<p>Abstrak <br />Terdapat banyak algoritma yang dapat dipakai untuk memprediksi arus lalu lintas, namun belum diketahui algoritma manakah yang memiliki kinerja lebih akurat untuk lalu lintas di Indonesia. Algoritma-algoritma tersebut perlu diuji untuk mengetahui algoritma manakah yang memiliki kinerja lebih akurat. Metode yang diusulkan adalah metode perbandingan tingkat akurasi dari algoritma berbasis neural network yang bisa digunakan untuk prediksi data rentet waktu. Algoritma yang akan diuji adalah back Propagation Neural Network (BP-NN), Adaptive Neuro Fuzzy Inference System (ANFIS), Wavelet Neural Network (WNN), dan Evolving Neural Network (ENN), yang digunakan untuk memprediksi arus lalulintas. Masing-masing algoritma akan implementasikan dengan menggunakan MatLab 2009b. Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) dan Mean Absolute Deviation (MAD). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dalam penelitian ini diketahui bahwa Algoritma ENN memprediksi arus lalu lintas dengan lebih akurat.</p>

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

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