General Regression Neural Network-Based Frame Work for the Evaluation of Ultimate Tensile Strength of Vibratory-Assisted Welded Joints

2021 ◽  
pp. 173-180
Author(s):  
M. Vykunta Rao ◽  
M. V. A. Raju Bahubalendruni ◽  
Vinod Babu Chintada
Keyword(s):  
Neural Network ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Welded Joints ◽  
General Regression Neural Network ◽  
Frame Work ◽  
General Regression

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

Vibratory weld conditioning during gas tungsten arc welding of al 5052 alloy on the mechanical and micro-structural behavior

2020 ◽  
Vol 17 (6) ◽  
pp. 831-836
Author(s):  
M. Vykunta Rao ◽  
Srinivasa Rao P. ◽  
B. Surendra Babu
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Welded Joints ◽  
Great Influence ◽  
Welding Process ◽  
Microstructural Characterization ◽  
Content Type ◽  
Average Grain Size

Purpose Vibratory weld conditioning parameters have a great influence on the improvement of mechanical properties of weld connections. The purpose of this paper is to understand the influence of vibratory weld conditioning on the mechanical and microstructural characterization of aluminum 5052 alloy weldments. An attempt is made to understand the effect of the vibratory tungsten inert gas (TIG) welding process parameters on the hardness, ultimate tensile strength and microstructure of Al 5052-H32 alloy weldments. Design/methodology/approach Aluminum 5052 H32 specimens are welded at different combinations of vibromotor voltage inputs and time of vibrations. Voltage input is varied from 50 to 230 V at an interval of 10 V. At each voltage input to the vibromotor, there are three levels of time of vibration, i.e. 80, 90 and 100 s. The vibratory TIG-welded specimens are tested for their mechanical and microstructural properties. Findings The results indicate that the mechanical properties of aluminum alloy weld connections improved by increasing voltage input up to 160 V. Also, it has been observed that by increasing vibromotor voltage input beyond 160 V, mechanical properties were reduced significantly. It is also found that vibration time has less influence on the mechanical properties of weld connections. Improvement in hardness and ultimate tensile strength of vibratory welded joints is 16 and 14%, respectively, when compared without vibration, i.e. normal weld conditions. Average grain size is measured as per ASTM E 112–96. Average grain size is in the case of 0, 120, 160 and 230 is 20.709, 17.99, 16.57 and 20.8086 µm, respectively. Originality/value Novel vibratory TIG welded joints are prepared. Mechanical and micro-structural properties are tested.

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