Application of Cascade-Correlation Algorithm in Vibration Characteristics of Hydro Turbine

2010 ◽  
Vol 108-111 ◽  
pp. 1070-1074
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Jian Min Hou
Keyword(s):  
Bp Neural Network ◽  
Learning Algorithm ◽  
Optimal Operation ◽  
Vibration Characteristics ◽  
Bp Algorithm ◽  
Practical Significance ◽  
Hydro Turbine ◽  
Correlation Algorithm ◽  
Cascade Correlation ◽  
Better Than

The cascade correlation algorithm that is CC algorithms, CC network structure and CC network weights learning algorithm are introduced, based on the operation data of Wanjiazhai hydropower station, considering the pressure fluctuation, the network model of vibration characteristics is established based on CC algorithm, and the applications of CC and BP algorithm in vibration characteristics of turbine are compared. The results show that the CC algorithm is better than BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance.

Application of Cascade-Correlation Algorithm in Cavitation Characteristics of Hydro Turbine

2010 ◽  
Vol 113-116 ◽  
pp. 250-253
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Bp Neural Network ◽  
Learning Algorithm ◽  
Real Data ◽  
Optimal Operation ◽  
Bp Algorithm ◽  
Practical Significance ◽  
Hydro Turbine ◽  
Correlation Algorithm ◽  
Cascade Correlation ◽  
Better Than

The cascade correlation algorithm that is cascade-correlation(CC) algorithms, CC network structure and CC network weights learning algorithm are introduced, based on the real data in hydropower station, considering the cavitation characteristics, the network model is established based on CC algorithm, and the applications of CC and BP algorithm of turbine are compared. The results show that the CC algorithm is better than BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance.

Application of ANN Trained with GA in Energy Characteristics of Hydraulic Turbine

2010 ◽  
Vol 108-111 ◽  
pp. 692-695
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Chuan Hong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Hydraulic Turbine ◽  
Optimal Operation ◽  
Practical Significance ◽  
Hydraulic Unit ◽  
Energy Characteristics ◽  
Artificial Neural Network Ann ◽  
The Relationship

The learning algorithm of artificial neural network (ANN) trained with genetic algorithm (GA) are introduced, based on the operation data of hydropower station, the network model of energy characteristics is established based on GA-ANN, the relationship curve between head H and output N is gained under some efficiency. The results show that the algorithm is better than BP neural network and avoid the limitations of BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance. The results show the new model has a great importance in hydraulic unit study. It could be generalized into other all efficiency prediction, and it offers a new way in water conservancy and at the meantime a new method for the study of ANN and GA.

BP Neural Network Learning Algorithm and its Software Implementation

2014 ◽  
Vol 513-517 ◽  
pp. 738-741 ◽  
Author(s):  
Ying Jian Lin ◽  
Xiao Ji Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Software Implementation ◽  
Voice Conversion ◽  
Bp Network ◽  
Widespread Application ◽  
Neural Network Learning ◽  
Basic Work

BP neural network in character recognition, pattern classification, text and voice conversion, image compression, decision support and so on aspects has the widespread application, in view of the problems existing in the actual application, this paper researches learning algorithm and software implementation. Learning algorithm studies include three aspects, illustrates the basic thoughts of the BP algorithm, designed the three layers BP network structure, the mathematical model for the accurate description of algorithm. Software implementation studies include two aspects, the network model of all neurons become linked list structure and storage structure is designed, the design of the software process and will implement the process into four steps. BP algorithm of the software implementation is a basic work for the application of BP neural network, using the research results of this paper, the user can easily neural network design and simulation.

scholarly journals Research on Premium Income Prediction Based on LSTM Neural Network

2019 ◽  
Vol 6 (11) ◽  
pp. 256-260
Author(s):  
Li Diao ◽  
Ning Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Practical Significance ◽  
Monthly Data ◽  
Loss Compensation ◽  
Long Short Term Memory ◽  
Premium Income ◽  
Social Management ◽  
Better Than

As one of the four financial pillars, insurance has the functions of risk diversification, loss compensation, financing and social management. It is of great practical significance to predict the level of premium income in the new normal of economy. In this paper, long short-term memory (LSTM) neural network was innovatively applied to the study of premium income prediction. The monthly data of China's premium income from January 1999 to October 2019 was selected for prediction, and the prediction results were compared with BP neural network. The results show that LSTM model can accurately predict premium income, and its performance is better than BP neural network.

Steel Pip Corrosion Forecasting Based on Support Vector Machine

2012 ◽  
Vol 166-169 ◽  
pp. 1002-1006
Author(s):  
Guang Yue Ma
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Support Vector ◽  
Risk Minimization ◽  
Prediction Ability ◽  
Structural Risk ◽  
Better Than ◽  
Local Extreme

BP neural network has some shorcomings,such as local extreme. Support vector machine is a novel statistical learning algorithm,which is based on the principle of structural risk minimization. In the paper, support vector machine is used to perform steel pip corrosion forecasting.The collected steel pip corrosion forecasting experimental data are given,among which corrosion deeps from 8ths to 11ths are used to test the proposed prediction model. BP neural network is applied to steel pip corrosion deep forecasting,which is used to compare with support vector machine to show the superiority of support vector machine in steel pip corrosion forecasting.The comparison of the prediction error of steel pip corrosion deep between support vector machine and BP neural network is given. It can be seen that the prediction ability for steel pip corrosion deep of support vector machine is better than that of BP neural network

Study on the Effectiveness Evaluation of the Weapon Equipment Based on Artificial Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3262-3265
Author(s):  
Feng Gao ◽  
Fei Song ◽  
Guo Qing Huang ◽  
Mao Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Effectiveness Evaluation ◽  
Matlab Toolbox ◽  
New Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Better Than

A new approach to weapons and equipment effectiveness evaluation based on artificial neural network (ANN) performs better than traditional method, which is in view of the complex relationship between the effectiveness and many factors that influence the evaluation. The structure and learning algorithm of BP neural network is evaluated the fighters’ air-to-air combat capability. The evaluation is accomplished by a two-layer BP neural network and MATLAB toolbox. The simulation results show that the artificial neural network have better generalization ability and approximation performance for continuous function, which is valuable in weapons and equipment effectiveness evaluation application.

Method of AC Servo System Based on Genetic Algorithm and Neural Network PID Control

2013 ◽  
Vol 717 ◽  
pp. 563-567 ◽  
Author(s):  
Wen Chun Chang ◽  
Cheng Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Network Structure ◽  
Bp Neural Network ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Bp Network ◽  
Neural Network Learning

BP network model has become one of the important neural network model, is used in many fields, but it has some defects. As from a mathematical perspective, it is a nonlinear optimization problem, which inevitably has the local minima problem; BP neural network learning algorithm has slow convergence rate, and the convergence speed and the initial weights of choice; network structure, namely the hidden layer nodes selection is still no theory until, but according to the experience. Based on the BP algorithm the local extreme values, considering the genetic algorithm and BP algorithm is combined with, on the BP neural network optimization. Neural network using genetic algorithm optimization mainly includes three aspects: the connection weights of evolution, evolutionary network structure, learning the rules of evolution.

Studies of an Improved LMBP Model for Predicting Potential Distribution of Insects

2012 ◽  
Vol 459 ◽  
pp. 594-598
Author(s):  
Yuan Ping Ni ◽  
Hui Ye
Keyword(s):  
Bp Neural Network ◽  
Traditional Method ◽  
Potential Distribution ◽  
Back Propagation ◽  
Fruit Fly ◽  
Bp Algorithm ◽  
Bactrocera Dorsalis ◽  
Oriental Fruit Fly ◽  
Lm Algorithm ◽  
Better Than

An improved LMBP algorithm model was presented based on analysing the shortcomings of back propagation (BP)neural network and discussing the idea of Levenberg-Marquardt (LM)algorithm. The simulating data proved that the improved LMBP model was able to overcome its local minimum and increase the converging speed in comparison with BP algorithm. Meanwhile this model was applied to predicting the potential distribution of insects. Here we take an example of oriental fruit fly Bactrocera dorsalis. The experimental results show that the algorithm model has a capacity of learning and can solve the forecasting problem in potential distribution of oriental fruit fly. The algorithm model is better than other traditional method and is very useful and efficient in practice

scholarly journals The Evaluation of Dynamic Airport Competitiveness Based on IDCQGA-BP Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Cui ◽  
Hai-bo Kuang ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Bp Algorithm ◽  
Quantum Genetic Algorithm ◽  
Competition And Cooperation ◽  
Better Than

Aimed at the multidimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP). The new algorithm is better than existing algorithms in convergence and the diversity of quantum chromosomes. The empirical data of eight airports in Yangtze River Delta in 2011 and 2012 is applied to verify the feasibility of the new algorithm, and then the competitiveness of the eight airports from 2013 to 2015 is gotten through the algorithm. The results show the following. (1) The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time. (2) The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger. (3) The main increase reason of airport competitiveness is the increase of own investment.

The Study on Neural Network Intelligent Method Based on Genetic Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 546-551
Author(s):  
Hong Tao Wang
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Computational Intelligence ◽  
Hybrid Algorithm ◽  
Learning Algorithm ◽  
Convergence Property ◽  
Bp Algorithm ◽  
Global Convergence Property ◽  
Simulation Results ◽  
Better Than

The paper gives the hybrid computational intelligence learning algorithm with global convergence, which is combined by BP algorithm and genetic algorithm. This algorithm connects the strengths of the BP algorithm and genetic algorithms. It not only has faster convergence, but also has a good global convergence property. The computer simulation results show that the hybrid algorithm is significantly better than the genetic algorithm and BP algorithm.

Sign in / Sign up

Export Citation Format

Share Document