FPGA-implementation of a Prediction Module Based on a Generalized Regression Neural Network

Krutikov, Alexander K.; Meltsov, Vasily; Lapitsky, Alexey A.; Rostovtsev, Vladimir S.

doi:10.1109/eiconrus49466.2020.9039414

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Diagram of generalized regression neural network structure was show in Figure 3. The generalized regression neural network protects the input layer, model layer, summing layer and output layer (Krutikov, Meltsov, Lapitsky, & Rostovtsev, 2020). The dimension of the input layer is n, and the linear function is the transfer function.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

Jia¹,

Wei²,

Zhang³

et al. 2021

GEP

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The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a certain amount of resource input. This paper selects the irrigation amount, precipitation and yield of winter wheat at the Wuqiao Scientific Observation and Experimental Station. Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks. This paper uses support vector machine (SVM), thought evolution algorithm to optimize BP neural network (MAE-BP) and generalized regression neural network (GRNN) to fit the water requirement and yield of two crops. The SVM is the model with the highest fitting accuracy among the three models, the RMSE, MAE, NS and R2 between predictive value and true value are 7.45 kg/hectares, 213.64 kg/hectares, 0.8086, 0.9409 respectively.

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Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

Jia¹,

Wei²,

Zhang³

et al. 2021

GEP

View full text Add to dashboard Cite

show abstract

“…It can also be used for dataset preprocessing when analyzing big data [2][3] or for creating training samples. The algorithm can be realized by both software and hardware [4].…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of the algorithm for distributing numerics by ranges

Strabykin,

Meltsov,

Krutikov

2023

E3S Web Conf.

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The paper considers an accelerated algorithm for distributing numbers over given ranges. The proposed algorithm allows you to quickly distribute integers over predetermined ranges with deterministic boundaries and calculate the number of elements in each of the ranges. If the number of ranges lies in the range from 64 to 256, then the time for solving the problem will be reduced by 9–33 times. The algorithm can be implemented in both software and hardware.

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Improving the Efficiency of Forecasting Sports Events Using a Cascade of Neural Networks

Meltsov

Krutikov

Strabykin

2022

Communication and Intelligent Systems

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FPGA-implementation of a Prediction Module Based on a Generalized Regression Neural Network

Cited by 8 publications

References 11 publications

Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

Improving the performance of the algorithm for distributing numerics by ranges

Improving the Efficiency of Forecasting Sports Events Using a Cascade of Neural Networks

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