Predicting Coal Ash Fusion Temperature Using Hybrid of Ant Colony Algorithm and BP Neural Network

2006 ◽  
Author(s):  
Yanpeng Liu ◽  
Mingguang Wu ◽  
Jixin Qian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Coal Ash ◽  
Ant Colony ◽  
Fusion Temperature ◽  
Ash Fusion Temperature

scholarly journals Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

2016 ◽  
Vol 40 ◽  
pp. 05010 ◽  
Author(s):  
Suzhen Miao ◽  
Qingyin Jiang ◽  
Hua Zhou ◽  
Jia Shi ◽  
Zhikai Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Coal Ash ◽  
Fusion Temperature ◽  
Modeling And Prediction ◽  
Ash Fusion Temperature

scholarly journals Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Na Jiang ◽  
Zhiwei Zhao ◽  
Pan Xu
Keyword(s):  
Neural Network ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Chronic Liver ◽  
Ant Colony ◽  
Assessment Model ◽  
Disease Assessment ◽  
Proposed Model

Timely prediction of the mechanism and characteristics of chronic liver disease using next-generation information technology is an effective way to improve the diagnosis rate of chronic liver disease. In this paper, we have proposed a modified backpropagation (BP) neural network with improved ant colony optimization algorithm to process multiple index attribute items describing chronic liver disease and construct a chronic liver disease assessment model. The proposed model is very effective in detecting chronic liver disease on time with acceptable level of accuracy and precision ratio. To verify these claims, the proposed scheme is checked experimentally where 125 groups of 20-dimensional medical test index data items of patients with chronic liver disease were analyzed. Moreover, 13-dimensional index items were preferentially selected as test index attribute items with high sensitivity to chronic liver disease using well-known ROC curves. The 13-dimensional index items were reduced to 5-dimensional comprehensive data items by principal component analysis. The proposed neural network-based model was trained with 115 sets of test indicator sample sets, and the remaining 10 sets of sample sets were used as test samples. Compared with the original 20-dimensional data as the neural network input, the proposed model not only reduces the complexity but also improves the prediction accuracy by 15.07%.

scholarly journals Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Fang Liu ◽  
Hua Gong ◽  
Ligang Cai ◽  
Ke Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Data Augmentation ◽  
Storage Period ◽  
Small Sample ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Bp Network ◽  
Data Pretreatment

Storage reliability is an important index of ammunition product quality. It is the core guarantee for the safe use of ammunition and the completion of tasks. In this paper, we develop a prediction model of ammunition storage reliability in the natural storage state where the main affecting factors of ammunition reliability include temperature, humidity, and storage period. A new improved algorithm based on three-stage ant colony optimization (IACO) and BP neural network algorithm is proposed to predict ammunition failure numbers. The reliability of ammunition storage is obtained indirectly by failure numbers. The improved three-stage pheromone updating strategies solve two problems of ant colony algorithm: local minimum and slow convergence. Aiming at the incompleteness of field data, “zero failure” data pretreatment, “inverted hanging” data pretreatment, normalization of data, and small sample data augmentation are carried out. A homogenization sampling method is proposed to extract training and testing samples. Experimental results show that IACO-BP algorithm has better accuracy and stability in ammunition storage reliability prediction than BP network, PSO-BP, and ACO-BP algorithm.

Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

2014 ◽  
Vol 704 ◽  
pp. 257-260
Author(s):  
De Wen Cai ◽  
Chen Fei Shao ◽  
Di Kai Wang ◽  
Er Feng Zhao ◽  
Meng Yang
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Ant Colony ◽  
Monitoring Data ◽  
Neural Network Modeling

Back Propagation (BP) neural network can learn and store a large number of input-output model nonlinear relationships with simple structure. Niche ant colony algorithm (NACA) combines the ant colony algorithm (ACA) with the niche technology in order to add its local search ability to ACA with preserving the intelligent search ability and robustness of ACA. To optimize predicting model establishment of the dam monitoring data, NACA and BP neural network modeling method are combined to establish a prediction model of horizontal displacement monitoring data. The traditional BP neural network prediction model is established to make a comparison with the NACA. The results show that NACA-BP neural network method can speed up the convergence rate of BP neural network and enhance local search ability and prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document