PM2.5 Prediction Model Based on ABC-BP

Wang, Qiang; Xu, Zhixiang; Xu, Zhengjun; Shi, Yuyang; Wu, Hailong; Shen, Guifang

doi:10.1109/cisce.2019.00039

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…2. Experiment results show that ABC-BPNN method fits the prediction trend more accurately, and has faster convergence and more stable prediction results (Liu and Meng 2019;Wang et al 2019).…”

Section: Adaptive Fuzzy Semi-parametric Regression Model Using Tpb An...mentioning

confidence: 96%

An adaptive fuzzy semi-parametric regression model using TPB and ABC-BPNN

Jiang

Lü

2023

Preprint

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In this paper, a new hybrid method is developed that incorporates the rationale of truncated power basis (TPB) and artificial bee colony-back propagation neural networks (ABC-BPNN) into an adaptive fuzzy semi-parametric regression model yielding more accurate results and better generalization ability. The proposed adaptive fuzzy semi-parametric regression model comprises sub-model formulation and approximates the observed fuzzy outputs from the outside employing neural networks computation, such that the proposed adaptive fuzzy regression model better explains the inherent dependence and vagueness that exist in a given dataset. Using the cross-validation criterion and absolute deviation-based distance measures for LR-type fuzzy numbers, a target function optimization problem of constructing the adaptive fuzzy semi-parametric regression is performed by solving the smooth function, bandwidth of kernel function and regression coefficients. This strategy significantly increases the goodness of fit for the proposed algorithm and offers a dependence framework among magnitude and uncertainty for the fuzzy regression model. We also use three formula measures to evaluate the fit quality of the regression results within each membership function as well as in the center or spread tendency property, respectively. The proposed algorithm is numerically evaluated on three experimental examples including a simulation study and two practical cases to prove its practicality and efficiency. Comparative analyses of our proposed method are provided in order to support its cogency, and the results show that our proposed model is more effective and stable than some other existing fuzzy regressions.

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Section: Adaptive Fuzzy Semi-parametric Regression Model Using Tpb An...mentioning

confidence: 96%

An adaptive fuzzy semi-parametric regression model using TPB and ABC-BPNN

Jiang

Lü

2023

Preprint

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“…Therefore, using PSO to optimize neural networks is not the best choice. Work [24] proposed a method through using ABC to optimize BPNN to find the best weights and thresholds. This method utilizes bees to effectively find high-quality food.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection for Network Based on Elite Clone Artificial Bee Colony and Back Propagation Neural Network

Zhou

Jia

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of Internet technology, network attacks have become more frequent and complex, and intrusion detection has also played an increasingly important role in network security. Intrusion detection is real-time and proactive, and it is an indispensable technology under the diversified trend of network security issues. In terms of network security, neural networks have the characteristics of self-learning, self-adaptation, and parallel computing, which are very important in intrusion detection. This paper combines back propagation neural network (BPNN) and elite clone artificial bee colony (ECABC) to propose a new ECABC-BPNN, which updates and optimizes the settings of traditional BPNN weights and thresholds. Then, apply ECABC-BPNN to network intrusion detection. Use the attack data samples of KDD CUP 99 and water pipe for attack classification experiments using GA-BPNN, PSO-BPNN, and ECABC-BPNN. The results show that the ECABC-BPNN proposed in this paper has an accuracy rate of 98.08% on KDD 99 and 99.76% on water pipe data. ECABC-BPNN effectively improves the accuracy of network intrusion classification and reduces classification errors. In addition, the time complexity of using ECABC-BPNN to classify network attacks is relatively low. Therefore, ECABC-BPNN has superior performance in network intrusion detection and classification.

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