Comparison of the GRNN and BP neural network for the prediction of populus (P.×euramericana cv.“74/76”) seedlings' water consumption

Abstract

water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of p.xeuramericana cv."74176", and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor.Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

Calculation of Resonating Frequency of an Equilateral Triangular Microstrip Antenna using Artificial Neural Network

“…In hidden layer function is sigmoid and in output layer function is linear. The ANN can be divided into two types-the forward and the feedback by the direction of the signal transmission [13]. The training of neural network performs in 10000 epochs.…”

Yarn Unevenness Prediction using Generalized Regression Neural Network

“…The generalized regression neural network (GRNN) proposed by DF (1991) converges to the optimal regression surface with more sample randomization accumulation and also has a good prediction effect when few data samples are present [7]. As a result of that GRNN has been used for many industrial sectors,including predicting water consumption of Populus euphratica seedlings [8], electricity price [9], road accident risk [10], key biological parameters in marine protease fermentation process [11], transformer health indicators [12], response of grade 6 titanium wire-cutting machine tool [13], pearlite layer spacing and mechanical properties related to alloy elements [14], wear AA219 graphite (GR) composites characteristics under diverse opportunities and standards [15], underground evaporation rate in arid areas [16] and others [17][18][19]. But its application in the textile prediction field is still limited.…”