scholarly journals Brief Paper: Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

2019 ◽  
Vol 6 (2) ◽  
pp. 87-90
Author(s):  
Haneet Kour ◽  
Jatinder Manhas ◽  
Vinod Sharma
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 5 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen I. Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

APLIKASI SISTEM CERDAS UNTUK PREDIKSI ENERGI LISTRIK PEMAKAIAN SENDIRI DI PT INDONESIA POWER SUB UNIT PLTA KABUPATEN WONOGIRI

2017 ◽  
Vol 8 (2) ◽  
pp. 489
Author(s):  
Herliyani Hasanah ◽  
Nurmalitasari Nurmalitasari
Keyword(s):  
Fuzzy Inference System ◽  
Input Data ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy Input

Kebutuhan akan energi listrik menjadi kebutuhan primer nasional. Dalam keberlangsungan proses produksi energi listrik pada pembangkitan – pembangkitan diperlukan energi listrik untuk pemakaian sendiri. Dalam penelitian ini dibangun sebuah aplikasi sistem cerdas untuk memprediksi energi listrik pemakaian sendiri di PT Indonesia Power sub unit PLTA Wonogiri. Pada penelitian ini menggunakan 2 kelompok input, yaitu input FIS (Fuzzy Inference System) dan input pada NN (Neuro Fuzzy). Input data  merupakan data produksi harian energi listrik di PLTA Wonogiri selama kurun waktu 2010 – 2016. Variabel data yang digunakan dalam penelitian ini adalah data produksi listrik untuk pemakaian PLTA Wonogiri adalah energi listrik yang dihasilkan PLTA Wonogiri dengan satuan KwH (f), elevasi muka air waduk dengan satuan meter (a1) dan debit air yang masuk ke turbin dengan satuan /detik (a2).  Output yang diperoleh adalah pusat centroid (m), derajat keanggotaan (mf), bobot (w) dan konsekuen parameter ( c ). Dari hasil pengujian diperoleh keluaran dengan performansi yang optimal pada saat Fuzzy C Means 2 kelas dengan parameter laju pembelajaran 0.4, momentum 0.6 dengan bessar Mean Percentage Error 0.377970875.  Kata kunci:  prediksi, pemakaian sendiri, energi listrik, fuzzy inference system, neuro fuzzy

scholarly journals Spike discharge prediction based on Neuro-fuzzy system

2017 ◽  
Author(s):  
Mahdi Zarei
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Subtractive Clustering ◽  
Inference System ◽  
Spike Discharge ◽  
Neuro Fuzzy ◽  
Spike Latency ◽  
Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

scholarly journals Predicting H2S emission from gravity sewer using an adaptive neuro-fuzzy inference system

2021 ◽  
Author(s):  
R. Salehi ◽  
S. Chaiprapat
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Predictive Performance ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Hydraulic Conditions ◽  
Gravity Sewer ◽  
H2s Emission ◽  
Gp Model

Abstract A predictive model to estimate hydrogen sulfide (H2S) emission from sewers would offer engineers and asset managers the ability to evaluate the possible odor/corrosion problems during the design and operation of sewers to avoid in-sewer complications. This study aimed to model and forecast H2S emission from a gravity sewer, as a function of temperature and hydraulic conditions, without requiring prior knowledge of H2S emission mechanism. Two different adaptive neuro-fuzzy inference system (ANFIS) models using grid partitioning (GP) and subtractive clustering (SC) approaches were developed, validated, and tested. The ANFIS-GP model was constructed with two Gaussian membership functions for each input. For the development of the ANFIS-SC model, the MATLAB default values for clustering parameters were selected. Results clearly indicated that both the best ANFIS-GP and ANFIS-SC models produced smaller error compared with the multiple regression models and demonstrated a superior predictive performance on forecasting H2S emission with an excellent R2 value of >0.99. However, the ANFIS-GP model possessed fewer rules and parameters than the ANFIS-SC model. These findings validate the ANFIS-GP model as a potent tool for predicting H2S emission from gravity sewers.

scholarly journals Performance of Clustering on ANFIS for Weather Forecasting

2018 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Candra Dewi
Keyword(s):  
Fuzzy Inference System ◽  
Rainy Season ◽  
Fuzzy Inference ◽  
Weather Forecasting ◽  
Dry Season ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Total Data ◽  
Dry Seasons

This paper proposes the comparison of using K-Means and Fuzzy C-Means (FCM) to optimize the premise parameters on Adaptive Neuro-Fuzzy Inference System (ANFIS) for weather forecasting. The ANFIS architecture groups each of the feature inputs in the first layer into three clusters, and uses three rules for the second layer. The comparison is performed based on the RMSE value and the number of iteration. The testing is done on the percentage of 40%, 50%, and 60% of the total data. In addition, the testing is done by grouping the data based on season called rainy and dry seasons. The testing results show that both K-Means and FCM havealmost the same RMSE, except for rainy season where K-Means has better RMSE. However, K-Means requires relatively more iterations to achieve convergence. The use of FCM, in general, gives better results than K-Means. It is also shown that ANFIS provides the best performance for data onto the dry season.

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