Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 79-85 ◽  
Author(s):  
Feng Yu ◽  
Zhi Qing Wang ◽  
Xiao Zhong Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Learning Ability ◽  
Time Frequency ◽  
Proposed Model ◽  
Real Coded Genetic Algorithm ◽  
Self Learning ◽  
Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

Short-Term Load Forecasting Based on GA-Elman Model

2014 ◽  
Vol 986-987 ◽  
pp. 520-523
Author(s):  
Wen Xia You ◽  
Jun Xiao Chang ◽  
Zi Heng Zhou ◽  
Ji Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Relative Error ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Elman Neural Network ◽  
Learning Efficiency ◽  
Multiple Layer ◽  
Short Term Load Forecasting

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

scholarly journals Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power System ◽  
Bp Neural Network ◽  
Normal Load ◽  
Load Forecasting ◽  
System Optimization ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

Study of a New Wavelet Neural Network of Image Compression Simulation

2012 ◽  
Vol 490-495 ◽  
pp. 623-627
Author(s):  
Xue Zhang Zhao ◽  
Qun Qi
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Bp Neural Network ◽  
Local Area ◽  
Wavelet Neural Network ◽  
Convergence Speed ◽  
Good Time ◽  
Time Frequency ◽  
Hidden Layer ◽  
Self Learning

In the practical need in order to make the most effective image compression in this paper, a new image compression used wavelet neural network model, and gives the corresponding calculation formula and algorithm procedures, By using wavelet transform good time-frequency local area on the characteristics and neural network self-learning function characteristics, overcome traditional BP neural network of hidden-layer points are difficult to be determined and the convergence speed is slow and easy to converge to a local minimum points shortcomings. The results of the simulation experiment prove wavelet neural network image compression characteristic and the convergence speed are much better than traditional BP neural network, and show that the algorithm is effective and feasible.

scholarly journals A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Zhongwei Li ◽  
Beibei Sun ◽  
Yuezhen Xin ◽  
Xun Wang ◽  
Hu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Large Scale ◽  
Culture Conditions ◽  
Computational Method ◽  
Learning Ability ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Medicinal Value

Flavones, the secondary metabolites ofPhellinus igniariusfungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from naturalPhellinuscan not meet the medical and research need, sincePhellinusin the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture ofPhellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.

Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1245-1252
Author(s):  
Ying Ying Feng ◽  
Nan Mu Hui ◽  
Zong An Luo ◽  
Dian Hua Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Hydraulic System ◽  
Learning Ability ◽  
Hydraulic Control ◽  
Control Effect ◽  
Hydraulic Control System

For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

scholarly journals Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hua Peng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Credit Rating ◽  
Hybrid Technique ◽  
Neural Network Approach ◽  
Enterprise Credit ◽  
The Neural Network ◽  
Proposed Model ◽  
Rating Model

In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

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