A Novel Nonlinear Chaotic Mapping Based on Cosine-Exponent

Jia, Jinwei; Han, Zhen; Liu, Limin

doi:10.1109/icdsca56264.2022.9988231

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Chaos is a nonlinear natural phenomenon, which has the advantages of ergodicity and randomness. At present, there are various chaotic maps to choose from in the field of optimization, including Logistic mapping, Tent mapping, and Arnold cat mapping [34]. The sequence of Logistic mapping is non-uniform: the sequence is characterized by a relatively uniform intermediate probability, but with a higher probability at both ends [35].…”

Section: Improved Sparrow Search Algorithmmentioning

confidence: 99%

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Yu,

Luo,

Bai

et al. 2024

Agronomy

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To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

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Section: Improved Sparrow Search Algorithmmentioning

confidence: 99%