HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations

An innovative neural-fuzzy clustering method is for predicting cluster (anomaly / background) of each new sample with the probability of its presence. This method which is a combination of the Fuzzy C-Means clustering method (FCM) and the General Regression Neural Network (GRNN), is an attempt to first divide the samples in the region by fuzzy method with the probability of being in each cluster and then with the results of this Practice, the artificial neural network is trained, and can analyze the new data entered in the region with the probable percentage of the clusters. More clearly, after a full mineral exploration, the sample can be attributed to a certain probable percentage of anomalies. To test the accuracy of this clustering in the form of the theory alone, a case study was conducted on the results of the analysis of regional alluvial sediments data in Birjand, IRAN, which resulted in satisfactory results. This software is written in MATLAB and its first application in mining engineering. This algorithm can be used in other similar applications in various sciences.

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GENERAL REGRESSION NEURAL NETWORK MODELING OF SOIL CHARACTERISTICS FROM FIELD TESTS

International Journal of Geomate ◽

10.21660/2017.29.5197j ◽

2017 ◽

Author(s):

Dahlia H. Hafez

Keyword(s):

Neural Network ◽

Network Modeling ◽

Field Tests ◽

Soil Characteristics ◽

General Regression Neural Network ◽

Neural Network Modeling ◽

General Regression

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

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096512666190118160604 ◽

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.

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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 ◽

10.1186/s13007-021-00714-9 ◽

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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