Yield prediction of apricot using a hybrid particle swarm optimization-imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method

Esfandiarpour-Boroujeni, I.; Karimi, Ehsan; Shirani, H.; Esmaeilizadeh, Majid; Mosleh, Z.

doi:10.1016/j.scienta.2019.108756

Cited by 38 publications

(16 citation statements)

References 49 publications

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“…SVR has been widely used in agriculture, imaging, localization and medicinal chemistry elds. [24][25][26] However, there is no unied standard for the selection of SVR parameters, which needs to be determined in accordance with specic applications. 27 In this paper, a machine learning model based on the SVM is proposed to establish a perdition model for the change of the medicinal efficacy of MC/MCC.…”

Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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“…Stochastic algorithms can be combined to generate hybrid meta-heuristics. In this case, the particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and support vector regression (SVR) were combined to predict the performance of apricot crops in Abarkuh Yazd (Iran): 61 variables were considered, 18 of those were more influential on the crops' performance according to the use of the hybrid algorithm [22]. Another manner to accurately estimate the performance of a crop is using data and indexes from crop-growing simulation tools.…”

Section: Related Researchmentioning

confidence: 99%

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

2020

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Agricultural field's production is commonly measured through the performance of the crops in terms of sow amount, climatology, and the type of crop, among other. Therefore, prediction on the performance of the crops can aid cultivators to make better informed decisions and help the agricultural field. This research work presents a prediction on wheat crop using the fuzzy set theory and the use of optimization techniques, in both; traditional methods and evolutionary meta-heuristics. The performance prediction in this research has its core on the following parameters: biomass, solar radiation, rainfall, and infield's water extractions. Besides, the needed standards and the efficiency index (EFI) used come from already developed models; such standards include: the root-mean-square error (RMSE), the standard deviation, and the precision percentage. The application of a genetic algorithm on a Takagi-Sugeno system requires and highly precise prediction on wheat cropping; being, 0.005216 the error estimation, and 99.928 the performance percentage.

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“…Herein, each feasible solution can be abstract as a particle with position x and velocity v, and the updating of the states are presented as equation ( 7) and (8). The fundamental factors are presented as TABLE II, and it is worthy to mention that the developed method recognizes the factors as constant, while other adaptive methods might be developed for enhancing the performances [24].…”

Section: B Parameter Identificationmentioning

confidence: 99%

“…Associated parameters are presented as TABLE II, and it is worthy to mention that the adopted cooling scheme is based on the particle swarm of PSO, and the factors are determined after the initialization of PSO. Herein, various methods can be applied for determining the factors of PSO [24,25], and it is considered to be appropriate that the inertia weight should be in 0 -1, and the learning factors can be in 0 -2.…”

Section: B Parameter Identificationmentioning

confidence: 99%

“…To explore more effective information, the definition of information entropy composites expectation of information contents, and the utilization of logarithm guarantee the monotonic for method. Considering that the constant C has no contribution for the information entropy, the normalized entropy is adopted as equation (24). The monotonic information entropy indicates the larger entropy usually refers to effective content.…”

Section: B) Information Entropy Theorymentioning

confidence: 99%

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A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

et al. 2020

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Considered to be the state-of-art solution for intelligent management of electric vehicles, cloud-control has been broadly investigated especially for parameterization and state estimation. Considering the operational cloud-database, the sampling intervals contribute to the precision and robustness of the battery management, and a balance between storage and performance is of crucial importance for real-time controlling. Unfortunately, the comprehensive performances on variable sampling intervals are doubtful for the development of cloud-control. Herein, the research of sampling intervals is carried out and the operational applications are simulated for validation including the precision, robustness and information content. 5 th -order spherical simplex-redial Kalman filter and particle swarm optimizationsimulated annealing methods are developed for researching the influences on precision of state of charge (SOC) estimation and parameterization under stable or dynamic conditions. Moreover, the information content of the desecrated database is evaluated based on the information entropy. The comparative results are carried out for separate characteristic, and the analysis exhibit the special performances for diverse sampling interval. The research confirms the differences on diverse intervals on operational applications, and the analysis might deliver effective guidance for future processing framework based on cloudcontrolling towards specific intentions.

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Yield prediction of apricot using a hybrid particle swarm optimization-imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method

Cited by 38 publications

References 49 publications

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

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