Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm

2014 ◽  
Vol 1014 ◽  
pp. 329-332
Author(s):  
Wei Ping Wang ◽  
Li Zhou

For the current smart building energy control algorithms are still large energy loss, poor energy-saving effect and other issues, this paper presents a fuzzy neural network algorithm based on improved BP algorithm, the improved algorithm of BP neural network algorithm first reverse dissemination and weighting coefficients are adjusted to accelerate the convergence rate of the original algorithm, and then build the improved BP neural network algorithm for fuzzy neural network, and then to improve it fuzzy membership function parameters to improve the efficiency of fuzzy neural network learning. Simulation results show that the proposed fuzzy neural network algorithm based on improved BP algorithm in the intelligent building energy control, with the algorithm is better than traditional BP neural network energy savings, reducing the energy loss rate.

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.


2014 ◽  
Vol 686 ◽  
pp. 388-394 ◽  
Author(s):  
Pei Xin Lu

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.


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