General Regression Neural Network Versus Back Propagation Neural Network for Prediction of Reheater and Super Heater Sprays in Thermal Power Plants
K.S. Madhavan
K.S Madhavan, Sr. DGM SBE, BHEL Corporate R&D, Vikasnagar, Hyderabad (Telangana), India.
Manuscript received on 25 February 2020 | Revised Manuscript received on 05 March 2020 | Manuscript Published on 15 March 2020 | PP: 90-93 | Volume-9 Issue-4S2 March 2020 | Retrieval Number: D10140394S220/2020©BEIESP | DOI: 10.35940/ijitee.D1014.0394S220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Neural Network models are used for Reheater and Super heater spray prediction in Thermal Power Plants. This paper makes a comparative study of the General Regression Neural Network (GRNN) model versus the Back propagation Neural Network (BPNN) model for the quality and accuracy of prediction of Reheater and Super heater Sprays in Thermal Power Plants. It proves that GRNN is better and gives more stable prediction within range; the glitches between the predicted and actual values being less in number as well as value.
Keywords: Back Propagation Neural Network, General Regression Neural Network, Reheater Spray, Super Heater Spray.
Scope of the Article: Network Architectures