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

Shi, Li; Ding, Xuehong; Li, Min; Liu, Yuan

doi:10.1155/2021/5554215

Cited by 21 publications

(8 citation statements)

References 30 publications

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“…The key to this method is the selection of an appropriate nonlinear function, which reduces the computational effort of the algorithm and improves the encryption efficiency. The study introduces the basic principles of the Sparrow Search Algorithm (SSA) [21]:…”

Section: Embedded Database Sqlite Encryption Algorithmmentioning

confidence: 99%

SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

Shi

2023

Journal of Applied Mathematics

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With the widespread use of embedded systems, chaos is a nonlinear system with certainty and complexity. It is an important topic in the field of information security at present, and it is an effective way to apply to embedded systems. It has great practical value in theory and in practice. This research mainly focuses on the encryption technology of SQLite embedded database and proposes an improved sparrow algorithm (Logistic Chaos Sparrow Search Algorithm, LCSSA) based on Logistic Chaos Map. It shows that the security level of SQLite in web development is higher than that of conventional Access. The population is initialized by the logistic chaotic mapping method, which improves the quality of the initial solution, increases the diversity of the population, and reduces the risk of premature maturity of the algorithm. The initial value y 0 determines the encryption method of the nonlinear function. Taking the integer variable (int) as an example, the value range is -231~231. It can be seen that the key space is sufficient to prevent various conventional attacks. When the key is the wrong key, decryption will not yield any data. It can be found that encryption and decryption are very sensitive to the key, which is also determined by the sensitivity of chaotic encryption system to the initial value. The benchmark function compares the performance of the improved algorithm with the algorithm before the improvement and compares it with the SSA. The LCSSA has better convergence performance, higher accuracy, and better stability.

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Section: Embedded Database Sqlite Encryption Algorithmmentioning

confidence: 99%

SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

Shi

2023

Journal of Applied Mathematics

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“…To verify the superiority of AVC-IMOA for optimizing the weights and thresholds of the BPANN, a BPANN based on the gradient descent method, a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP) [29], a BPANN based on the standard artificial bee colony algorithm (ABC_BP) [30] and a BPANN based on an improved sparrow search algorithm (FASSA_BP) [31] are selected as comparison algorithms. In addition, in order to fairly compare the performance and the accuracy of prediction of the different models, the structure of the BPANN is 5-8-1, the population size of the four intelligent optimization algorithms is 40, the sigmoid function is used as the transfer function, and the maximum running time of the five algorithms is 60 s. The prediction results and prediction accuracy are shown in Table 7.…”

Section: Prediction Of Pork Supplymentioning

confidence: 99%

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

et al. 2022

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Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.

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“…However, neural networks are applied in a process where the initial weights and thresholds of the neural network are assigned randomly, and different initial values have a large impact on the prediction results [17]. The Sparrow Search Algorithm (SSA) takes into account all the possible factors of the group's behavior, allowing it to quickly converge to the neighborhood of the optimum [18,19]. Therefore, this paper proposes a machine learning model based on the Improved Sparrow Search Algorithm (ISSA) to optimize the Back-Propagation neural network (BPNN) for SST retrieval.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search Algorithm

Ji,

Ding

2023

Remote Sensing

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Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network’s initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters.

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

Cited by 21 publications

References 30 publications

SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search Algorithm

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