International Journal of Neural Networks and Advanced Applications

   
ISSN: 2313-0563
Volume 7, 2020

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 7, 2020


Title of the Paper: An Overview of Data Mining with Neural Network

 

Authors: U. C. Jha

Pages: 16-18

https://doi.org/10.46300/91016.2020.7.2

Abstract: With increasing data base management systems applications, large amounts of important data are available much of its knowledge is preserved and concealed. The methods used to extract data from is Data Mining. Various tools are available to forecast the trends that will support decision of people. Neural Networks or Artificial Neural Networks (ANN) have been a promising system in many applications due to their learning ability from data and generalization ability. Neural Networks are used for prediction, classification, forecasting, and pattern recognition. This paper provides a brief overview of Data Mining with the Neural Network, its tools and process


Title of the Paper: A Novel Adaptive Speed Observer Using Neural Network and Sliding-Mode for SPIM Drives

 

Authors: Ngoc Thuy Pham

Pages: 1-15

https://doi.org/10.46300/91016.2020.7.1

Abstract: In this paper, a novel Stator Current Based Model Reference Adaptive System (SC_MRAS) speed estimation scheme using neural network (NN) and Sliding Mode (SM) is proposed to improve the performance of the MRAS speed observer for high-performance Six Phases Induction Motor (SPIM) drives, especially at low and zero speed region, where the poor performance of observers is still always a large challenge. In this novel SC_MRAS scheme, a two-layer linear NN, which has been trained online by means of the Total Least Squares (TLS) algorithm, is used as an adaptive model to estimate the stator current and this model is employed in prediction mode. These novel proposed can ensure that the whole drive system achieves faster satisfactory torque and speed control and strong robustness, the observer operate better accuracy and stability both in transient and steady-state operation. Especially, in this proposed observer, the rotor flux, which is needed for the stator current estimation of the adaptive model and providing to the controller, is identified based on adaptive SM technique. The improvement of Rotor Flux Estimation for SC_MRAS-Based Sensorless SPIM Drives help to eliminate the disadvantages in SC_MRAS based observer such as stator resistance sensitivity, and flux open loop integration which may cause dc drift and initial condition problems or instability in the regenerating mode of operation, therefore, enhancing the rotor flux estimation, speed estimation and control accuracy at very low and zero stator frequency operation help improve the overall observer and drive system performance. The indirect field oriented control (IFOC) for speed control of a sensorless SPIM drive using the proposed observer is built by MATLAB/ Simulink. The simulation results are presented under sensorless speed control performance to validate the effectiveness of the proposed estimation and control algorithms.