2 edition of Adaptive neuro-fuzzy modeling of electric arc furnaces. found in the catalog.
Adaptive neuro-fuzzy modeling of electric arc furnaces.
Written in English
|The Physical Object|
|Number of Pages||193|
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can Cited by: Abstract: This work presents a neuro-fuzzy model for solving the medium – term electric load-forecasting. The model used a time series of monthly data. The load forecasting is based on Adaptive Neural Fuzzy Inference System (ANFIS). The results are compared to those of an Autoregressive (AR) model and an Autoregressive Moving Average model.
In this paper, we present a novel adaptive neuro-fuzzy Inference system (ANFIS) for edge detection of an image. The key features of our approach which differentiate us from others is the use of image content and adaptive neuro-fuzzy Inference system for edge detection of application-specific image. works is called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. modeling. neuro-fuzzy control. ANFIS.
The previous research on adaptive neuro-fuzzy inferential systems (ANFIS) presented an approach to estimating the average indoor temperature in the building environment. However, the restriction on robustness limited the energy efficiency and indoor comfort ratio. An accurate and robust prediction model is proposed in this paper. Comparing to the previous unphysical rules based ANFIS Cited by: 3. neuro-fuzzy systems is presented for modeling the subsystems of the heat recovery steam generator (HRSG). The dynamic neuro-fuzzy models were developed based on the formal NARX models topology. The clustering techniques were employed to define the structure of the fuzzy models by dividing the entire operating regions into smaller subspaces.
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This paper presents an application of adaptive neuro-fuzzy networks which dynamically reconstructs the model of nonlinear v-i characteristic in electric arc furnaces. Electric arc furnaces. Presents the application of adaptive fuzzy logic systems to modelling electric arc furnaces.
The main objectives are to provide the rationale and to justify the use of fuzzy modeling for electric. This research presents an adaptive neuro-fuzzy system which is used in the current prediction through the electric arc from an electric arc furnace.
Electric arc furnaces are complex systems that produce many problems, mainly harmonic currents, reactive energy, flicker effect, by: 3.
Modelling and simulator development for electric arc furnaces (EAFs) are of significant importance in designing control systems and in performance optimisation of EAFs. This paper presents a method based on adaptive neuro-fuzzy inference systems (ANFIS) for modelling and simulating EAFs with the focus on the regulator loop that is used for Cited by: Generally, it is necessary to maintain constant arc lengths for these kinds of furnaces.
The two control strategies proposed in this paper regulates the current of the electric arc because arc length depends by the electric arc current. In order to do this, a new model of the electric arc developed by the authors of this paper was : Loredana Ghiormez, Octavian Prostean, Manuela Panoiu, Caius Panoiu.
This paper presents an arc furnace model using adaptive neuro-fuzzy inference system (ANFIS) in order to capture random, non-linear and time-varying load pattern of an arc furnace.
To evaluate the performance of the proposed model, several case studies are presented where the outputs of the proposed model are compared with the data recorded in. An adaptive neuro-fuzzy model is used to represent the vibration in the UHA helicopter.
The results indicate that neuro-fuzzy modeling can be successfully used to model and predict the UHA vibration to less than g’s (average error). Since the proposed model converges very fast, it can be potentially used for real time predictions.
The adaptive neuro-fuzzy inference system (ANFIS), developed in the early s by Jang, combines the concepts of fuzzy logic and neural networks to form a hybrid intelligent system that enhances the ability to automatically learn and adapt hybrid systems have been used by researchers for modeling and predictions in various engineering systems Cited by: The validation of the model was tested by four random data sets.
To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of by: 2.
Adaptive Neuro-Fuzzy System for Current Prediction in Electric Arc Furnaces. Pages Panoiu, Manuela (et al.) Soft Computing Applications Book Subtitle Proceedings of the 6th International Workshop Soft Computing Applications (SOFA ), Volume 1. ANFIS modeling process starts by obtaining a data set (input–output data) and dividing it into training and checking data sets.
Fig. The tool-workpiece thermocouple experimental setup 3. ANFIS MODELING Figures Adaptive neuro-fuzzy inference system (ANFIS) is an architecture which is functionallyAuthor: Pavel Kovač, Dragan Rodić, Marin Gostimirović, Borislav Savković, Dušan Ješić. Adaptive Neuro-Fuzzy Controller of Switched Reluctance Motor 29 The parameters to be trained are ai, bi, and ci of the premise parameters and pi, qi, and ri of the consequent parameters.
Training algorithm requires a training set defined between inputs and output [10 - 12]. An Adaptive Neuro-Fuzzy Inference System (ANFIS) modelling of oil is used for modeling the effect of important parameters on oil retention in a carbon dioxide air-conditioning system is trained and tested with the experimental data taken from the experimental work The refrigeration loop consisted of a compressor driven by an electric.
Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems S. Jassara,*,1, Z. Liaob, L. Zhaoa aDepartment of Electrical and Computer Engineering, Ryerson University, Victoria Street, Toronto, ON, Canada M5B2K3 b Department of Architectural Science, Ryerson University, Canada article info.
Modeling And Field Oriented Control Of Induction Motor By Using An Adaptive Neuro Fuzzy Interference System Control Technique 77 the detailed description of ANFIS will be discussed in the following section.
III. ADAPTIVE NEURO FUZZY INTERFERENCE SYSTEM Adaptive neuro-fuzzy inference system is theCited by: 1. Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process Using CNT Infused Copper Electrode Prabhu Sethuramalingam1* Oliver Nesa Raj Sundararaj1 1Department of Mechanical Engineering, SRM University, Chennai, India * Corresponding author’s Email: [email protected] by: 2.
ORIGINAL ARTICLE An adaptive neuro fuzzy model for estimating the reliability of component-based software systems Kirti Tyagi a,*, Arun Sharma b a Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India b Department of Computer Science and Engineering, Krishna Institute of Engineering and Technology, Ghaziabad, India.
The Adaptive Neuro-Fuzzy Inference System (ANFIS), developed in the early 90s by Jang , combines the concepts of fuzzy logic and neural networks to form a hybrid intelligent system that enhances the ability to automatically learn and adapt. Hybrid systems. genetic algorithms and neuro-fuzzy algorithms yield a suitable solution for controlling nonlinear systems; besides, they can be used for modeling, prediction and optimization of complex systems.
This paper deals with the control of the IPMC actua-tors by using an adaptive neuro-fuzzy control. The neuro-fuzzy. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS).
This intelligent estimator is implemented using Matlab/Simulink and the Cited by:. Manuela PANOIU, Loredana GHIORMEZ, Caius PANOIU, Adaptive Neuro-Fuzzy System for Current Prediction in Electric Arc Furnaces, Advances in Intelligent Systems and Computing – Book series, vol 1Proceedings of the 6th International Workshop Soft Computing Applications (SOFA ), pag.SpringerLink, ISI Proceedings1 b.A novel architecture of hierarchical adaptive neuro-fuzzy inference systems was developed, which was tuned using a genetic algorithm and particle swarm optimization algorithm, separately.
It was used to establish input-output relationships of a plasma spray coating by: 6.M. Huang, et al., in carried an integrated approach for glaucoma detection using adaptive neuro fuzzy inference system.
The purpose of this study was to develop an automated classifier based on adaptive neuro fuzzy inference system, to differentiate between normal and .