scholarly journals Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network

2019 ◽  
Vol 2019 (16) ◽  
pp. 1423-1428 ◽  
Author(s):  
Rumeng Wang ◽  
Ming Zhang ◽  
Yunpeng Jiang ◽  
Yong Yang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
General Regression Neural Network ◽  
Adaptive Mutation ◽  
Contamination Degree ◽  
General Regression

The Construction and Analysis of a Prediction Model for Combining the Taguchi Method and General Regression Neural Network for Injection Moulding

2005 ◽  
Vol 13 (8) ◽  
pp. 823-829
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Yung-Chang Li ◽  
Yi-Shiuan Wu
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Prediction Model ◽  
Injection Moulding ◽  
Processing Parameters ◽  
General Regression Neural Network ◽  
Dimensional Error ◽  
Moulding Process ◽  
Injection Moulding Process ◽  
General Regression

Plastics always show mould deformation after the injection moulding process and this can cause dimensional errors. The extent of a dimensional error can be controlled effectively by choosing adequate processing parameters. The target of this work was to reduce the dimensional error in the hexagonal screw diameter when processing polyether ether ketone (PEEK), and to improve the polymer's quality. First, we selected the factor levels according to the chosen quality characteristics. We decided that a prediction model for the injection process could be constructed using the Taguchi quality method and a general regression neural network (GRNN). The optimum conditions determined by the Taguchi method can be modified by the neural network. The injection moulding product can achieve a reduced dimensional error by adjusting the factor levels using this combined approach.

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.

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