A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines

2007 ◽  
Vol 37 (5) ◽  
pp. 1321-1331 ◽  
Author(s):  
Zhan-Li Sun ◽  
Kin-Fan Au ◽  
Tsan-Ming Choi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Extreme Learning Machines ◽  
Inference System ◽  
Learning Machines ◽  
Neuro Fuzzy

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

2021 ◽  
pp. 014459872110417
Author(s):  
Ya-Jun Fan ◽  
Hai-tong Xu ◽  
Zhao-Yu He
Keyword(s):  
Fuzzy Logic ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Hybrid Control ◽  
Inference System ◽  
Neuro Fuzzy

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

scholarly journals An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

scholarly journals An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
M. Ajay Kumar ◽  
N. Srikanth
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Pulse Width ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Light Transmission ◽  
Inference System ◽  
Hvdc Light ◽  
Neuro Fuzzy

AbstractIn HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

Neuro-Fuzzy-Based Control for Parallel Cascade Control

2013 ◽  
Vol 8 (1) ◽  
pp. 15-25 ◽  
Author(s):  
R. Karthikeyan ◽  
K. Manickavasagam ◽  
Shikha Tripathi ◽  
K.V.V. Murthy
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Internal Model Control ◽  
Cascade Control ◽  
Regulatory Control ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy ◽  
Model Control

Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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 

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