A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology

2020 ◽  
Vol 34 (10) ◽  
pp. 3363-3385
Author(s):  
Alireza Farrokhi ◽  
Saeed Farzin ◽  
Sayed-Farhad Mousavi
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Inference System ◽  
Temporal Variability ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Inference System ◽  
Analysis Methodology ◽  
Neuro Fuzzy ◽  
Innovative Trend Analysis ◽  
New Framework

scholarly journals Identifikasi Individu Berdasarkan Pola Citra Rugae Palatina Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 12 (2) ◽  
pp. 156
Author(s):  
Farah Hana Kusumaputri ◽  
Suryo Adhi Wibowo ◽  
Yuti Malinda
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Natural Disasters ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Component Analysis ◽  
Inference System ◽  
Identification Process ◽  
Neuro Fuzzy

Abstract Indonesia is a country that is in an area prone to natural disasters, such as volcanic eruptions, earthquakes, tsunamis, and others. These natural disasters often cause many victims to die. To identify the victims' identities, an identification process is needed. The identification method most commonly used today is using fingerprints, teeth, and DNA. However, this identification method still has some shortcomings. At present a more effective alternative method is offered by utilizing the palatine rugae pattern. Rugae palatina has individual characteristics and is resistant to all kinds of damage. So that Rugae palatina has the potential to be used in the process of individual identification. In this research, application of palatine rugae image processing application will be developed with data recording, image registration, feature extraction using Principal Component Analysis (PCA) method, and palatine rugae pattern classification using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The expected output from this final project is a system that is able to identify individuals by utilizing the palatine rugae pattern. To get good and effective parameters for system performance, periodic testing is carried out. The sampling procedure uses original photographs directly taken from the palatine rugae, so that it will facilitate the identification process. Keyword: ANFIS, ANN, Fuzzy Logic, PCA, rugae palatina Abstrak Negara Indonesia merupakan negara yang berada di daerah rawan bencana alam, seperti erupsi gunung merapi, gempa bumi, tsunami, dan lain-lain. Bencana alam tersebut seringkali menyebabkan korban meninggal dalam jumlah yang banyak. Untuk mengenali identitas para korban tersebut diperlukannya proses identifikasi. Metode identifikasi yang paling sering digunakan saat ini yaitu menggunakan sidik jari, gigi, dan DNA. Namun, metode identifikasi tersebut masih mempunyai beberapa kekurangan. Saat ini ditawarkan metode alternatif yang lebih efektif yaitu dengan memanfaatkan pola rugae palatina. Rugae palatina memiliki sifat yang individual dan tahan terhadap segala macam kerusakan. Sehingga Rugae palatina memiliki potensi untuk digunakan dalam proses identifikasi individu. Dalam penelitian ini akan dikembangkan aplikasi pengolahan sampel citra rugae palatina dengan proses perekaman data, registrasi citra, ekstrasi ciri menggunakan metode Principal Component Analysis (PCA), dan klasifikasi pola rugae palatina menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS). Keluaran yang diharapkan dari penelitian ini adalah sebuah sistem yang mampu mengidentifikasi individu dengan memanfaatkan pola rugae palatina. Untuk mendapatkan parameter yang baik dan efektif terhadap performansi sistem, maka dilakukan pengujian secara berkala. Prosedur pegangambilan sampel menggunakan foto asli yang secara langsung diambil dari rugae palatina, sehingga akan mempermudah proses identifikasi. Kata kunci: ANFIS, ANN, Fuzzy Logic, PCA, rugae palatina 

Nonlinear System Identification of Smart Buildings

2017 ◽  
pp. 328-347
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

Nonlinear System Identification of Smart Buildings

Fuzzy Systems ◽  
2017 ◽  
pp. 1183-1202
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

Sign in / Sign up

Export Citation Format

Share Document