This paper proposed a hybrid technique based on power quality (PQ) enhancement in grid connected Photovoltaic (PV) system. The hybrid technique is the combined performance of both the Radial Basis Function Neural Network (RBFNN) and Proportional Integral (PI) controller. The primary intention of the proposed method is to predict the adaptive gain parameters for both the normal and abnormal environment in the grid side. In the proposed method, the RBFNN is trained with input parameters such as grid power variations and the target gain parameters of the PI controller. During the testing time, the RBFNN predicts the gain parameters of the PI controller as per the grid side parameter variation and the PQ of the grid side has been enhanced. Then the proposed method is implemented in the MATLAB/Simulink platform and the effectiveness is examined by comparison analysis with the conventional techniques. Ó 2016 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Sujatha B, et al. Electronic and ionic conductivity studies on microwave synthesized glasses containing transition metal ions. J Mater Res Technol. 2016. http://dx.
Texture Analysis plays an important role in the interpretation, understanding and recognition of terrain, biomedical or microscopic images. To achieve high accuracy in classification the present paper proposes a new method on textons. Each texture analysis method depends upon how the selected texture features characterizes image. Whenever a new texture feature is derived it is tested whether it precisely classifies the textures. Here not only the texture features are important but also the way in which they are applied is also important and significant for a crucial, precise and accurate texture classification and analysis. The present paper proposes a new method on textons, for an efficient rotationally invariant texture classification. The proposed Texton Features (TF) evaluates the relationship between the values of neighboring pixels. The proposed classification algorithm evaluates the histogram based techniques on TF for a precise classification. The experimental results on various stone textures indicate the efficacy of the proposed method when compared to other methods.
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