Optimal selection of SOP and SPH using fuzzy inference system for on-line epileptic seizure prediction based on EEG phase synchronization

2019 ◽  
Vol 42 (4) ◽  
pp. 1049-1068 ◽  
Author(s):  
Hesam Shokouh Alaei ◽  
Mohammad Ali Khalilzadeh ◽  
Ali Gorji
Keyword(s):  
Epileptic Seizure ◽  
Fuzzy Inference System ◽  
Phase Synchronization ◽  
Fuzzy Inference ◽  
Optimal Selection ◽  
Seizure Prediction ◽  
Inference System ◽  
On Line ◽  
Epileptic Seizure Prediction ◽  
Selection Of

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

scholarly journals PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

2019 ◽  
Vol 8 (4) ◽  
pp. 451-461
Author(s):  
Khusnul Umi Fatimah ◽  
Tarno Tarno ◽  
Abdul Hoyyi
Keyword(s):  
Lagrange Multiplier ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lagrange Multiplier Test ◽  
Inference System ◽  
Input Selection ◽  
Lm Test ◽  
Rice Prices ◽  
Selection Of

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Genetic Algorithms ◽  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Vibration Data ◽  
Neuro Fuzzy ◽  
Machine Fault ◽  
The Time Domain ◽  
Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

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