Liuzhou Teachers College Liuzhou

Guangxi, China

Liuzhou Teachers College Liuzhou

Guangxi, China
Time filter
Source Type

Ding H.,Wuhan University of Technology | Ding H.,Liuzhou Teachers College Liuzhou | Dong W.,Hubei University
Soft Computing | Year: 2015

Because most of runoff time series with limited amount of data reveal inherently nonlinear and stochastic characteristics and tend to show chaotic behavior, strategies based on chaotic analysis are popular methods to analyze them from real systems in nonlinear dynamics. Only one kind of predicted method for yearly rainfall-runoff forecasting cannot achieve perfect performance. Thus, a mixture strategy denoted by WT-PSR-GA-NN, which is composed of wavelet transform (WT), phase space reconstruction (PSR), neural network (NN) and genetic algorithm (GA), is presented in this paper. In the WT-PSR-GA-NN framework, the process to deal with time series gathered from Liujiang River runoff data is given as follows: (1) the runoff time series was first decomposed into low-frequency and high-frequency sub-series by wavelet transformation; (2) the two sub-series were separately and independently reconstructed into phase spaces; (3) the transformed time series in the reconstructed phase spaces were modeled by neural network, which is trained by genetic algorithm to avoid trapping into local minima; (4) the predicted results in low-frequency parts were combined with the ones of high-frequency parts, and reconstructed with wavelet inverse transformation, to form the future behavior of the runoff. Experiments show that WT-PSR-GA-NN is effective and its forecasting results are high in accuracy not only for the short-term yearly hydrological time series but also for the long-term one. The comparison results revealed that the overall forecasting performance of WT-PSR-GA-NN proposed by us is superior to other popularity methods for all the test cases. We can conclude that WT-PSR-GA-NN can not only increase the forecasted accuracy, but also its own competitiveness in efficiency, effectiveness and robustness. © 2015 Springer-Verlag Berlin Heidelberg

Prediction of the performance of centrifugal compressors, the traditional methods using BP neural network. This single neural network for forecasting problem is not high enough precision, slow convergence and easy to fall into local optimal solution. In order to more accurately predict the performance of centrifugal compressors, the implicit commit identify problems early. Are the immune algorithm, genetic algorithm, wavelet theory, the combination of neural networks, established immune genetic algorithm optimization of wavelet neural network model (IGA-WNN). Realized to predict the performance of centrifugal compressor, and the predicted results with the BP neural network model prediction results and the wavelet neural network model prediction results were compared. Simulation results show that: the prediction model, can achieve the centrifugal compressor performance prediction and monitoring. Which, IGA-WNN optimal prediction results: with a simple algorithm, structural stability, the convergence speed and generalization ability of the advantages of prediction accuracy of 99% over traditional methods of prediction accuracy of 15%, with a certain Theoretical study and practical value. © 2011 ACADEMY PUBLISHER.

Xie J.,Liuzhou Teachers College Liuzhou | Lan J.,Liuzhou Teachers College Liuzhou | Yu L.,Liuzhou Teachers College Liuzhou | Wu C.,Liuzhou Teachers College Liuzhou
Advanced Materials Research | Year: 2012

The aim was to provide scientific basis for development and utilization of pinus elliottii engelm resources.As the raw material, pines needles of pinus elliottii were moistened first by vaporization medium. After that, the needles were processed in advance by microwave to change tissues of needles, and finally, the Shikimic acid was extracted from the needles in hot water. Meanwhile, the extraction method is compared with traditional one in the paper. As a result, the perfect technological conditions were gained for extraction of Shikimic acid by microwave preprocessing. The method is: moistening pine needles within 15 minutes in 70% ethanol water which was 1.6 times weight of needles, then, processing the needles for 60 seconds by 70W microwave. The processing time should increase 30 seconds every 5-gram material added. Finally, the extraction lasted over 30 minutes adding 25-time hot water at 80°C. Seven eighths of extraction time by microwave preprocessing is shortened compared by the traditional extraction method, and extraction ratio for Shikimic acid raises by 35.8%. © (2012) Trans Tech Publications, Switzerland.

Chen J.,Liuzhou Teachers College Liuzhou | Li J.,Liuzhou Teachers College Liuzhou
Procedia Engineering | Year: 2011

Based on linear matrix inequality (LMI), the problem of H ∞ guaranteed cost control for a class of descriptor system is addressed, where the uncertainties exist in the systematic matrix and the controller gain. Under the additive perturbation, the sufficient condition of non-fragile H ∞ guaranteed cost is presented, and corresponding controller design is given in terms of the feasible solutions of LMI. © 2011 Published by Elsevier Ltd.

Huang S.-Z.,Liuzhou Teachers College Liuzhou | Huang L.-Y.,Liuzhou Teachers College Liuzhou
2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010 | Year: 2010

In order to present the routing algorithms for currently communication networks, the constrained optimization problem was solved depending on the ability of the neural networks. The neural networks was used in optimum routing problem on in case of packet switched computer networks, and the main objects are decrease the mean delays in the communication networks. The correctness of neural network is verified by the conclusions of calculation of a neural design to solve the shortest path problem. Simulation model of neural network shown that the model can enable algorithm obtaining the minimum error. © 2010 IEEE.

Loading Liuzhou Teachers College Liuzhou collaborators
Loading Liuzhou Teachers College Liuzhou collaborators