An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

Jiang, Jianming; Feng, Ting; Liu, Caixia

doi:10.1155/2021/6691724

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…To avoid inherent errors, an improved nonlinear grey Bernoulli model was obtained. Results indicated that the prediction accuracy of this model was higher, and it was more suitable for these practical situations [9]. Tian et al proposed a new data-driven model to improve process data modeling accuracy.…”

Section: Related Workmentioning

confidence: 98%

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Zhao,

Liu,

2024

Journal of Applied Mathematics

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With the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy-tailed noise using the improved whale algorithm. Results showed that it had a lower rank average and an average success rate of 95.65%. It found the global optimum when the number of iterations reached around 150 and had faster convergence speed and accuracy. In identifying Hammerstein model under heavy-tailed noise, the average prediction recognition accuracy of the improved whale algorithm was 92.38%, the determination coefficient was 0.89, the percentage fitting error was 0.03, and the system error was 0.02. This research achievement has certain value in the field of industrial control and can serve as a technical reference.

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Section: Related Workmentioning

confidence: 98%

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Zhao,

Liu,

2024

Journal of Applied Mathematics

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“…Firstly, we compare the optimization algorithms. The results of OBLAOA are compared with the following algorithms: Arithmetic Optimization Algorithm (AOA), Whale Optimization Algorithm (WOA) [45], Salp Swarm Algorithm (SSA) [46], Weighted Salp Swarm Algorithms (WSSA) [47], Exponential Neighborhood Grey Wolf Optimization (ENGWO) [48], developed Arithmetic Optimization Algorithm (dAOA) [49] and improved arithmetic optimization algorithm (IAOA) [50]. Secondly, we compare our OBLAOA-DBSCAN algorithm with five classical clustering algorithms, namely K-means [51], Spectral [52], OPTICS [53], clustering by fast search and find of density peaks (DPC) [54] and the original DBSCAN.…”

Section: Experiments Settingsmentioning

confidence: 99%

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Yang

Chen

et al. 2022

J Supercomput

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As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

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“…The conditional independence of the Naive Bayes classifier limits the scope of application and classification accuracy of the Naive Bayes classification. The conditional independence of naïve Bayes classifier limits the scope of application and classification accuracy of naïve Bayes classification, Jiang Liqing et al [16] proposed to use of an improved whale optimization algorithm to optimize the naïve Bayes classifier, and the improved whale optimization algorithm uses a taboo search mechanism to jump out of the error that the algorithm is prone to fall into local optimization when the algorithm seeks optimization, and the algorithm automatically searches the global weights of the attributes of the classifier, thereby improving the accuracy of the weighted Bayesian classifier operation. Tan Zhang et al [17] based on the three existing classifier evaluation indicators of accuracy, recall rate, and F1-score, a new classifier evaluation index is designed, and through the classification experiment based on CNN and RNN classifiers, compared with the different performances of new and old indicators in the performance evaluation of classifiers, the results show that the evaluation of the classification results of the new indicators is more comprehensive, but the applicability of the new indicators to the classification of unbalanced data sets can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Pulse Bag Filter Fault Classifier Research and Analysis

Ding

Cao

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problem of fault alarm flooding, a fault classifier is established to automatically classify fault types in combination with unsupervised learning methods in machine learning. The k-means method is used to classify the alarm types in dimensionality reduction, and the optimal K value is selected as 11 through various evaluation indexes, and the faults are added to the new category labels according to the center of each cluster after clustering. The random forest and support vector classification algorithm is used to establish a fault classifier model on the fault alarm dataset with new labels, and the unified analysis of the fault type prediction results of the test set unlabeled fault data set is compared with the random forest algorithm, support vector classification, and k-means method. The results show that the unity rate of the prediction results of the three types of classifiers on the test set when taking different K values can reach up to 67%, and the uniformity rate of the prediction results of the three models exceeds 0.998, which has a high degree of uniformity, indicating that the establishment of the fault alarm classifier for the pulse bag dust collector is feasible and has good classification accuracy, which can help maintenance personnel quickly locate and troubleshoot faults, and improve the intelligence level of equipment.

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An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

Cited by 6 publications

References 45 publications

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Pulse Bag Filter Fault Classifier Research and Analysis

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