Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97939
Title: Complex-valued neuro-fuzzy inference system for wind prediction
Authors: Suresh, Sundaram
Subramanian, K.
Savitha, R.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Subramanian, K., Savitha, R., & Suresh, S. (2012). Complex-valued neuro-fuzzy inference system for wind prediction. The 2012 International Joint Conference on Neural Networks (IJCNN).
Abstract: In this paper, we present a complex-valued neuro-fuzzy inference system (CNFIS) and its gradient descent based learning algorithm developed employing Wirtinger calculus. The proposed CNFIS is a four layered network which realizes zero-order Takagi-Sugeno-Kang based fuzzy inference mechanism. CNFIS is used to predict the speed and direction of wind. Here, the speed and direction are considered as statistically independent variables and are represented as a complex-valued signal (with speed as magnitude and direction as phase). Performance of CNFIS is compared with other algorithms available in the literature and results indicate improved performance of CNFIS. The major contribution of this paper is as follows: (1) Propose a complex-valued neuro-fuzzy inference system (2) Employ Wirtinger calculus for complex-valued gradient descent algorithm (3) Solve wind speed and direction prediction problem in complex domain.
URI: https://hdl.handle.net/10356/97939
http://hdl.handle.net/10220/12380
DOI: http://dx.doi.org/10.1109/IJCNN.2012.6252812
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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