Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference system for draft force prediction of chisel plow implement

2017 ◽  
Vol 142 ◽  
pp. 406-415 ◽  
Author(s):  
S.M. Shafaei ◽  
M. Loghavi ◽  
S. Kamgar
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Force Prediction ◽  
Neuro Fuzzy ◽  
Draft Force ◽  
Chisel Plow ◽  
Takagi Sugeno

scholarly journals The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

2019 ◽  
Vol 50 (4) ◽  
pp. 991-1001 ◽  
Author(s):  
Mohammad Ashrafi ◽  
Lloyd H. C. Chua ◽  
Chai Quek
Keyword(s):  
Fuzzy Inference System ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Training Dataset ◽  
Rule Base ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
River Routing

Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.

scholarly journals Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 9 (2) ◽  
pp. 187
Author(s):  
Jani Kusanti ◽  
Sri Hartati
Keyword(s):  
Ischemic Stroke ◽  
Ct Scan ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Model ◽  
Cluster Center ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Global Threshold ◽  
Takagi Sugeno

AbstrakPenggunaan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam proses identifikasi salah satu gangguan neurologis pada bagian kepala yang dikenal dalam istilah kedokteran stroke ischemic dari hasil ct scan kepala dengan tujuan untuk mengidentifikasi lokasi  yang terkena stroke ischemik. Langkah-langkah yang dilakukan dalam proses identifikasi antara lain ekstraksi citra hasil ct scan kepala dengan menggunakan histogram. Citra hasil proses histogram ditingkatkan intensitas hasil citranya dengan menggunakan threshold otsu sehingga didapatkan hasil pixel yang diberi nilai 1 berkaitan dengan obyek sedangkan pixel yang diberi nilai 0 berkaitan dengan background. Hasil pengukuran digunakan untuk proses clustering image, untuk proses cluster image digunakan fuzzy c-mean (FCM). Hasil clustering merupakan deretan pusat cluster, hasil  data digunakan untuk membangun fuzzy inference system (FIS). Sistem inferensi fuzzy yang diterapkan adalah inferensi fuzzy model Takagi-Sugeno-Kang. Dalam penelitian ini ANFIS digunakan untuk mengoptimalkan hasil penentuan lokasi penyumbatan stroke ischemic. Digunakan recursive least square estimator (RLSE) untuk pembelajaran. Hasil RMSE yang didapat pada proses pelatihan sebesar 0.0432053, sedangkan pada proses pengujian dihasilkan tingkat akurasi sebesar 98,66% Kata kunci—stroke ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE  Abstract            The use of Adaptive Neuro Fuzzy Inference System (ANFIS) methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM) clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS). Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE) for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66% Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE 

A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications

2007 ◽  
Vol 11 (4) ◽  
pp. 365-372 ◽  
Author(s):  
Cheng-Jian Lin ◽  
◽  
Chi-Yung Lee ◽  
Cheng-Hung Chen ◽  
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Layer Structure ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Multi Level ◽  
Takagi Sugeno

In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.

Forecasting East Asian Tourist Arrivals to Thailand with Adaptive Neuro-Fuzzy Inference System

2019 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Chawalsak Phetchanchai ◽  
Chuthawuth Chantaramalee ◽  
Napatsarun Chatchawalanont ◽  
Piyapong Phatcha
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
East Asian ◽  
Time Lag ◽  
Training Dataset ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Takagi Sugeno

Objective - This research aims to propose the approach of forecasting tourist arrivals to Thailand. Methodology/Technique – Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as our forecasting method by using fuzzy C-means clustering as a technique for the partitioning training dataset Findings - The appropriate parameter of time lag was found for each dataset of East Asian tourist arrivals to Thailand. Novelty - The forecasting procedure with the appropriate parameter of time lag was represented our work as a novelty idea. Type of Paper: Empirical. Keywords: Tourist arrivals forecasting, East Asian countries, adaptive neuro-fuzzy inference system, fuzzy C-means clustering, Takagi–Sugeno fuzzy inference system.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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