Prediction model of PSO-BP neural network on coliform amount in special food

Deng, Yun; Xiao, Hanjie; Xu, Jian‐Xin; Wang, Hua

doi:10.1016/j.sjbs.2019.06.016

Cited by 66 publications

(29 citation statements)

References 8 publications

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“…To test the performance of the evaluation model, this paper analyzes and compares the performance of FASSA-BP, SSA-BP, PSO-BP, and BP algorithms in terms of the accuracy and reliability of the model prediction results. In order to enhance the scientific nature of the evaluation of the forecast results, multiple performance metrics are used to evaluate the accuracy and stability of IMCM evaluation model results [34][35][36][37][38]. e performance analysis indexes used in this paper are as follows:…”

Section: Selection Of Network Performance Indicatorsmentioning

confidence: 99%

Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network

Shi

Ding

et al. 2021

Complexity

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Intelligent manufacturing capability evaluation is the key for enterprises to scientifically formulate the implementation path and continuously improve the level of intelligent manufacturing. To help manufacturing enterprises diagnose the level of intelligent manufacturing capability, this paper conducts research on intelligent manufacturing capability maturity evaluation based on maturity theory. The evaluation problem is a complex nonlinear problem, and BP neural network is particularly suitable for solving such complex mapping problems. Aiming at the problem that the BP neural network is sensitive to initial weights and thresholds, the sparrow search algorithm (SSA) is used to optimize the initial weights and thresholds of the BP neural network. In order to overcome the shortcoming of SSA that it is easy to fall into the local optimum, the firefly disturbance strategy is introduced to improve it, a new sparrow search algorithm (FASSA) is proposed, and on this basis, an intelligent manufacturing capability maturity evaluation model based on the FASSA-BP algorithm is constructed. Finally, a large battery manufacturing enterprise in China is selected for empirical research, and the comparison experiments are carried out on the FASSA-BP model, BP model, SSA-BP model, and PSO-BP model in terms of accuracy, stability, etc. The results show that the evaluation of intelligent manufacturing capability maturity through this model can effectively help companies diagnose problems in the construction of intelligent manufacturing and provide a reference for companies to accurately improve their intelligent manufacturing capabilities.

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Section: Selection Of Network Performance Indicatorsmentioning

confidence: 99%

Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network

Shi

Ding

et al. 2021

Complexity

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“…A BP neural network is used in this system for wind power prediction where the environmental information, such as ambient temperature, wind speed and humidity are taken as the inputs. The network is extremely sensitive to the weighting factors, and different initial values will lead to different results [41]- [44]. Improper selection of the weights will incur the network oscillation or out of convergence.…”

Section: B Pso-bp Network For Wind Power Predictionmentioning

confidence: 99%

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

et al. 2020

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“…It is proved theoretically that the neural network of a single hidden layer can approximate the nonlinear function with arbitrary precision, so that the model can realize the nonlinear mapping from input to output. With the increase of the number of hidden layers, the output error of the network will decrease [28]. erefore, the increase of hidden layers will improve the accuracy of the network but will make the network structure become complex, the running time become longer, and even lead to the overfitting phenomenon.…”

Section: Basic Bpnn Modelmentioning

confidence: 99%

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

Chen

Dian

2020

Mathematical Problems in Engineering

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The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.

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Prediction model of PSO-BP neural network on coliform amount in special food

Cited by 66 publications

References 8 publications

Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network

Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

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