scholarly journals Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System

2015 ◽  
Vol 55 ◽  
pp. 1-11
Author(s):  
Abdur Rosyid ◽  
Mohanad Alata ◽  
Mohamed El Madany
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Element Model ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rotor Bearing ◽  
Lqr Controller

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

scholarly journals Perancangan Sistem Pendukung Keputusan Penentuan Impor Bawang Merah

2015 ◽  
Vol 2 (3) ◽  
pp. 181
Author(s):  
Wiwi Widayani ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Market Price ◽  
Training Data ◽  
Percentage Error ◽  
Inference System ◽  
Absolute Percentage Error ◽  
Testing Data ◽  
The Government

Pertambahan jumlah penduduk Indonesia serta meningkatkannya permintaan industri akan bawang merah yang tidak diimbangi dengan jumlah produksi mendorong pemerintah membuka impor bawang merah. Impor dilakukan untuk menjaga keseimbangan harga dan pasokan bawang merah sehingga inflasi yang diakibatkan kenaikan harga bawang merah dapat ditekan, namun impor yang tidak tepat jumlah akan mengakibatkan kerugian bagi pihak petani, perlu adanya sistem pendukung dalam menentukan volume impor guna menjaga keseimbangan harga pasar dan pemenuhan kebutuhan bawang merah. Sistem pendukung keputusan yang dirancang menerapkan Fuzzy Inference System (FIS) Tsukamoto. Sistem yang dirancang memungkinkan pengguna untuk melakukan training data dan testing data, proses dalam training data yaitu : 1)Clustering data latih, menggunakan algoritma K-Means 2)Ekstraksi Aturan, 3)Testing data latih, hitung nilai impor dengan fuzzy Tsukamoto, 4)Menganalisa error hasil fuzzy menggunakan MAPE(Means Absolute Percentage Error), 5)Testing Data Uji dan menganalisa hasil error data uji. Hasil Uji Model menunjukan penentuan impor bawang merah dengan parameter input harga petani, harga konsumen, produksi, konsumsi, harga impor dan kurs terhadap 60 data latih menghasilkan error terendah sebesar 0.07 pada 12 cluster, hasil uji mesin inferensi terhadap data uji menghasilkan error sebesar 0.25. Indonesian population growth and increase industrial demand shallot is not matched with number of production prompted the government to opened shallot imports. Import done to maintain the balance price and supply of shallot so inflation caused by rising prices of onion can be suppressed, but not the exact amount of imports would result in losses for the farmers, support system in determining volume imports is need to maintain balance of market price and needs of shallot. Decision support system designed to apply Fuzzy Inference System (FIS) Tsukamoto. The system is allows the user to perform the training data and testing data, the training process performs are: 1) Clustering training data, using the K-Means algorithm 2) Extraction Rule, 3) Testing data, calculate imports value by fuzzy Tsukamoto, 4) analyze the results error using MAPE (Means Absolute Percentage error), 5) testing test data and analyze the results error. The results show the determination of imported shallot with input parameters producer prices, consumer prices, production, consumption, import prices and the exchange rate against 60 training data produces the lowest error of 0:07 in 12 clusters, the inference engine test resulted in an error of 0.25.

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.

Roller Element Bearing Fault Size Estimation Using Adaptive Neuro-fuzzy Inference System (ANFIS)

2020 ◽  
Author(s):  
Sushant M. Patil ◽  
Ramchandra Ganapati Desavale ◽  
Surajkumar G Kumbhar
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Indicator ◽  
Size Estimation ◽  
Vibration Level ◽  
Inference System ◽  
Bearing Fault ◽  
Neuro Fuzzy ◽  
Rotor Bearing ◽  
Bearing System

Abstract The vibration level is a function of the defects in the bearing. By identifying a change in vibration level, one can predict the dynamic behaviour and fault in the rotor- bearing system. An imminent bearing fault detection can reduce downtime or avoid the failure of rotating machinery. The condition monitoring or maintenance schedule can be set well if the diagnosis estimate bearing fault size accurately. In view of this, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and dimension analysis (DA) was utilized to detect the bearing fault size. Several experiments were performed at different rotating speeds on the rotor-bearing system. Defects were created on bearing races artificially using electrode discharge machining (EDM) and the vibration responses are acquainted by accelerometer and Fast Fourier Techniques (FFT). With a 0.1 mm error band to fix minor bugs, a two-performance indicator evaluated the model accuracy. A comparison of the performance of models with experimental results and artificial neural network (ANN) are studied. The results showed that an ANFIS performs better over DA and ANN. This contributes to detecting bearing fault effectively and accuracy improvement in the estimation of the bearing fault size.

scholarly journals Basement Flood Control with Adaptive Neuro Fuzzy Inference System Using Ultrasonic Sensor

2020 ◽  
Vol 5 (2) ◽  
pp. 11
Author(s):  
Raden Muhamad Yuda Pradana Kusumah ◽  
Maman Abdurohman ◽  
Aji Gautama Putrada
Keyword(s):  
Control System ◽  
Water Level ◽  
Fuzzy Inference System ◽  
Flood Control ◽  
Fuzzy Inference ◽  
Flood Management ◽  
Ultrasonic Sensor ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a basement flood management system based on Adaptive Neuro Fuzzy Inference System (ANFIS). Basement is one of the main parts of a building that has a high potential for flooding. Therefore, the existence of a flood control system in the basement can be a solution to this threat. Water level control is the key to solving the problem. Fuzzy Inference System (FIS) has proven to be a reliable method in the control system but this method has limitations, that is, it needs to have a basis or a reference when determining the fuzzy set. When there is no basis or reference, Adaptive Neuro FIS (ANFIS) can be a solution. The Neuron aspect in ANFIS determines fuzzy sets through training data. In terms of the Internet of Things (IoT), this system uses an ultrasonic sensor, Node Red IoT platform, and Matlab Server.  Then the water pump will turn on to control the water level when there is rainfall. By undergoing a comparative test with the FIS method, ANFIS provides a lower Root Mean Square Error (RMSE) and is recommended for use in basement flood management systems.

scholarly journals An Optimal Approach of Adaptive Neuro -Fuzzy Inference System to Predict the Roof Ductility Demand of EBFs Subjected to Near-Fault Pulse-Like Ground Motions

2020 ◽  
Vol 5 (6) ◽  
pp. 1516-1537
Author(s):  
Seyed Abdonnabi Razavi ◽  
Navid Siahpolo ◽  
Mehdi Mahdavi Adeli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time History ◽  
Training Data ◽  
Subtractive Clustering ◽  
Inference System ◽  
Geometrical Properties ◽  
Near Fault ◽  
Neuro Fuzzy ◽  
Intelligent Model

Careful estimation of global ductility will certainly lead to greater accuracy in the design of structural members. In this paper, a new and optimal intelligent model is proposed to predict the roof ductility (μR) of EBF steel frames exposed to the near-fault pulse-like earthquakes, using the Adaptive Neuro-Fuzzy Inference System (ANFIS). To achieve this goal, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3-,6-, 9-, 12-, 15, 20-stories, steel EBF frames are considered with 3 different types of link beam, column stiffness, and brace slenderness. All models are analysed to reach 4 different performance levels using nonlinear time history under 20 near-fault earthquakes. About 6769 data are applied as ANFIS training data. Subtractive clustering and Fuzzy C-Mean clustering (FCM) methods are applied to generate the purposed model. The results show that FCM provides more accurate outcomes. Moreover, to validate the model, 2257 data are applied (as test data) in order to calculate the correlation coefficient (R) and mean squared error (MSE) between the predicted values of (μR) and the real values. The results of correlation analysis show the high accuracy of the proposed intelligent model.

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