RETRACTED ARTICLE: Product modeling design based on genetic algorithm and BP neural network

Han, Jia Liang; Yuan, Min; Wang, Kun

doi:10.1007/s00521-020-05604-0

Cited by 77 publications

(43 citation statements)

References 13 publications

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“…BP neural network is the most common neural network, which has a wide range of applications in various fields [ 21 ]. It can express the nonlinear mapping with high complexity, and the ecological status evaluation model of landscape is to describe the nonlinear mapping relationship between various indicators, so the BP neural network is used as the model [ 22 ].…”

Section: Construction Of the Landscape Ecological Evaluation Modelmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

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Section: Construction Of the Landscape Ecological Evaluation Modelmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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“…In this study, the BP model was used to estimate the TICS. The BP neural network consists of three layers: input layer, hidden layer, and output layer [ 27 ]. When it talks to the BP neural network estimation, the retrieving speed will increase and the amount of calculation will be decreased by reducing the number of characteristic variables.…”

Section: Methodsmentioning

confidence: 99%

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

et al. 2021

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Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

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“…The genetic algorithm has a strong global search capability, and the evolution process of each generation of the population can easily obtain the global optimal solution [30]. The neural network and genetic algorithm are combined to establish a microscale system for optimizing meniscus electrodeposition parameters, which can globally optimize process parameters to obtain the best combination [31].…”

Section: Parameter Optimization Based On Bp Neural Network and Geneti...mentioning

confidence: 99%

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Zhang

Rong

et al. 2021

Micromachines

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The rate and quality of microscale meniscus confined electrodeposition represent the key to micromanipulation based on electrochemistry and are extremely susceptible to the ambient relative humidity, electrolyte concentration, and applied voltage. To solve this problem, based on a neural network and genetic algorithm approach, this paper optimizes the process parameters of the microscale meniscus confined electrodeposition to achieve high-efficiency and -quality deposition. First, with the COMSOL Multiphysics, the influence factors of electrodeposition were analyzed and the range of high efficiency and quality electrodeposition parameters were discovered. Second, based on the back propagation (BP) neural network, the relationships between influence factors and the rate of microscale meniscus confined electrodeposition were established. Then, in order to achieve effective electrodeposition, the determined electrodeposition rate of 5 × 10−8 m/s was set as the target value, and the genetic algorithm was used to optimize each parameter. Finally, based on the optimization parameters obtained, we proceeded with simulations and experiments. The results indicate that the deposition rate maximum error is only 2.0% in experiments. The feasibility and accuracy of the method proposed in this paper were verified.

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RETRACTED ARTICLE: Product modeling design based on genetic algorithm and BP neural network

Cited by 77 publications

References 13 publications

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

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