Modeling and Analyzing of Regional Fluctuation of Cocoon Silk Based on BP Neural Network

2011 ◽  
Vol 175-176 ◽  
pp. 418-423
Author(s):  
Xi Yang ◽  
Bing Di Liu ◽  
Hai Liu ◽  
Lun Bai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Short Term ◽  
Test Analysis ◽  
Bp Network ◽  
Silk Cocoon ◽  
Bp Artificial Neural Network ◽  
Set Up ◽  
Short Term Prediction

According to the collected data of the market monthly closing price on dry cocoon and raw silk, predictive modeling and analyzing on the price trend of dry cocoon and raw silk are made based on the related theories and test analysis of BP Artificial Neural Network is carried out. Developed corresponding procedure with Neural Network toolbox under the condition of MATLAB, and then set up BP network relevant prediction models, at last, checked up with examples. At the same time, this study set up corresponding time series forecasting models and made empirical analysis on the basis of Eviews software. The results show that, the two methods both fit for short-term prediction, BP network can achieve human coordination control, the better predictive precision, which supplies an analysis way for silk cocoon market, all of that can be referred to in the future.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

Application of Data Fusion Based on Genetic Algorithm and BP Neural Network in WSN

2012 ◽  
Vol 532-533 ◽  
pp. 1606-1610 ◽  
Author(s):  
Yi Xu ◽  
Zhao Xiang Li ◽  
Jiao Jiao Liu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Mean Squared Error ◽  
Rates Of Convergence ◽  
Sensor Nodes ◽  
Bp Network ◽  
Bp Artificial Neural Network ◽  
Learning Rates ◽  
Individual Measure

Energy-saving is one of the inevitable problems of the routing design in WSN, while Data Fusion technology is widely utilized in energy constraint WSN to reduce the amount of messages exchanged between sensor nodes. This paper proposes a new algorithm based on Integrated Genetic and BP Neural Network(IGBP), IGBP uses the global search capability of GA to remedy the deficiency of BP artificial neural network. First, IGBP generates the best individuals in different networks by GA algorithm. Then it chooses the most optimize individual measure by Mean Squared Error to construct the BP network which was supplied to train of the WSN. Using the optimize individual nodes as initialization value training the BP network, it will enhance the learning rates of convergence and avoid falling into the local minimums .The simulation results show that the IGBP algorithm has made great progress in balancing the consumption of energy so as to prolong the network lifetime.

Prediction Models of Short-Term Traffic Flow Based on Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2908-2911 ◽  
Author(s):  
Chao Jun Dong ◽  
Ang Cui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Topology ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Measured Data ◽  
Short Term ◽  
Hidden Layer ◽  
Bp Neural Network Model

For the city’s road conditions, a nonlinear regression prediction model based on BP Neural Network was built. The simulation shows it has good adaptability and strong nonlinear mapping ability. Using the wavelet basis function as hidden layer nodes transfer function, a BP-Neural- Network-topology-based Wavelet Neural Network model was proposed. The model can overcome the defects of the BP Neural Network model that easy to fall into local minimum and cannot perform global search. The feasibility of the model was proved using measured data from yingbin avenue in jiangmen city.

Short-Term Prediction on the Time Series of PCP Speed Based on Elman Neural Network

2012 ◽  
Vol 569 ◽  
pp. 749-753
Author(s):  
Xiao Ren Lv ◽  
Xuan Luo ◽  
Shi Jie Wang ◽  
Rui Nie
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Varying ◽  
Rapid Convergence ◽  
Short Term ◽  
Elman Neural Network ◽  
Term Prediction ◽  
Short Term Prediction

Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

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