Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network

2014 ◽  
Vol 15 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Zhuomin Wang ◽  
Dongguo Shao ◽  
Haidong Yang ◽  
Shuang Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Water Transfer ◽  
General Regression Neural Network ◽  
Quality Data ◽  
Water Delivery ◽  
The South ◽  
North Water ◽  
General Regression

The safety of water delivery and water quality in the South to North Water Transfer Project of China is important to northern China. Water quality data, flow data and data on factors that influence water quality were collected from 25 May to 26 August, 2013. These data were used to forecast water quality and calculate the relative error when using a genetic algorithm optimized general regression neural network (GA-GRNN) model as well as conventional general regression neural network (GRNN) and genetic algorithm optimized back propagation (GA-BP) models. The GA-GRNN method requires few network parameters and has good network stability, a high learning speed and strong approximation ability. The overall forecasted result of GA-GRNN is the best of three models, of which the root mean square error (RMSE) of every index is nearly the least among three models. The results reveal that the GA-GRNN model is efficient for water quality prediction under normal conditions and it can be used to ensure the security of water delivery and water quality in the South to North Water Transfer Project.

scholarly journals Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4241
Author(s):  
Yi-Cheng Huang ◽  
Zi-Sheng Yang ◽  
Hsien-Shu Liao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Ccd Camera ◽  
Remaining Useful Life ◽  
General Regression Neural Network ◽  
Robot Arm ◽  
Feature Extraction Method ◽  
A Charge ◽  
General Regression

The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.

Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

2018 ◽  
Vol 27 (2) ◽  
pp. 291-302 ◽  
Author(s):  
Zhi-da Guo ◽  
Jing-Yuan Fu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Railway Freight ◽  
Accurate Forecast ◽  
Freight Volume ◽  
Construction Operation ◽  
General Regression

AbstractRailway freight transportation is an important part of the national economy. Accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision making of railways. After analyzing the application status of generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model by using improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of GRNN, and then the optimal spread is used for forecasting in GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared with the results of GRNN, a higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method.

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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