The Applications of GA-BP Neural Networks in Option Direction for Catalytic Agent

2014 ◽  
Vol 510 ◽  
pp. 45-50 ◽  
Author(s):  
Lai Jiang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Design Method ◽  
Neutral Network ◽  
Bp Neural Networks ◽  
Slow Convergence ◽  
Optimum Direction

Although the application of the BP neural network can assist researching on catalytic agent and methods of synthesizing material, but because of slow convergence, in some cases, it can not work efficiently. The paper presents a design method for the application of GA-BP Neutral Network in optimum direction for catalytic agent and material. The simulation result shows that the application of the GA-BP neural network can improve the accuracy and versatility of the catalyst compounding.

Study of Bidding Decision-Making in Construction Project Based on LM-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2339-2342 ◽  
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Bp Neural Network ◽  
Construction Project ◽  
Bp Neural Networks ◽  
Slow Convergence ◽  
Bidding Process

There is a tendency that arbitrary and tendentiousness are brought by experts’ subjectivity during the bidding evaluation. A method for construction project bidding based on the BP neural network improved by LM is proposed. To overcome the disadvantage of slow convergence and being prone to converge to minimum, LM was used to be combined with the BP neural networks. It has been proved by an application an effective complement for the deficiency of BP neural network on the bidding decision of construction project. Through the application in reality, we found that the LM-BP neural network in resolving the problems during the bidding process is meaningful.

Approximation Performance of BP Neural Networks Improved by Heuristic Approach

2013 ◽  
Vol 411-414 ◽  
pp. 1952-1955 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Numerical Optimization ◽  
Heuristic Algorithms ◽  
Nonlinear Function ◽  
Heuristic Approach ◽  
Optimization Approach ◽  
Bp Neural Networks ◽  
The One

Among all improved BP neural network algorithms, the one improved by heuristic approach is studied in this paper. Firstly, three types of improved heuristic algorithms of BP neural network are programmed in the environment of MATLAB7.0. Then network training and simulation test are conducted taking a nonlinear function as an example. The approximation performances of BP neural networks improved by different numerical optimization approaches are compared to aid the selection of proper numerical optimization approach.

Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Structural Damage ◽  
The State ◽  
Structural Damage Detection ◽  
Bp Neural Networks ◽  
Simply Supported ◽  
Modal Flexibility

Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

Prediction of Yarn Unevenness Based on BP Neural Network

2012 ◽  
Vol 627 ◽  
pp. 329-332
Author(s):  
Hui Jun Li ◽  
Xin Hou Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Test Results ◽  
Bp Neural Networks

The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on BP neural network. The BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on BP neural network are very precise and efficient.

The Application of BP Neural Network in Diagnosing the Short Circuit Fault of Winding

2014 ◽  
Vol 543-547 ◽  
pp. 1064-1067
Author(s):  
Jian Qun Zhang ◽  
De Jian Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Evidence Theory ◽  
Accurate Diagnosis ◽  
Bp Neural Networks ◽  
Rotor Winding ◽  
Short Circuit Fault

As a common fault of motor, the short circuit of rotor winding is important for the accurate diagnosis. In this article, the author collected every status parameter of motor by different sensors, using two BP neural networks to partly diagnose the motor and fusing the results of partly diagnosis by D-S evidence theory. The author increases the creditability of diagnosis results by practices and decreases uncertainty, showing the efficiency of this method.

Prediction of the Forest Health Based on BP Neural Networks

2013 ◽  
Vol 726-731 ◽  
pp. 4303-4306 ◽  
Author(s):  
Yong Wang ◽  
Zhuang Xiong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forest Health ◽  
Bp Neural Networks ◽  
Forest Disease ◽  
Bp Neural Network Model

This paper simple introduced back propagation (BP) neural networks, and constructed a dynamic predict model, based on it to predict forest disease and insect and rat pest. Then it analyzed and simulated with the BP neural network model with the data produced in the recent ten years. The result indicated that the BP neural network model is reliable for predicting the forest disease and insect and rat pest. The method provides scientific foundation for the forestry management of studied area.

Study on Construction Bidding Management Based on GA-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2423-2426
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Construction Project ◽  
Basic Theory ◽  
Slow Convergence ◽  
Artificial Neural

In the bidding evaluation, the deviations are likely to be brought about by experts' subjectivity, arbitrary and tendentiousness. A method for construction project bidding based on the BP neural network improved by GA (Genetic Algorithm) is proposed. On the basis of the basic theory of the BP neural network, discussions are provided on how to rectify the drawbacks of slow convergence and prone to convergence to minimum with the use of GA. The model is successfully applied GA - BP artificial neural networks to project, which are in concert with the result of experts. The study makes contribution to research about the evaluation system of construction bidding management.

Predicting Yarn Unevenness Using Improved BP Neural Network

2011 ◽  
Vol 331 ◽  
pp. 219-222
Author(s):  
Hui Jun Li ◽  
Xin Hou Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Test Results ◽  
Bp Neural Networks

The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on improved BP neural network. The improved BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on improved BP neural network are very precise and efficient.

A Simulation and Prediction Method of the Height of Wheat Based on BP Neural Networks and the Ant Colony Algorithm

2013 ◽  
Vol 659 ◽  
pp. 113-117 ◽  
Author(s):  
Hong Yi Li ◽  
Xi Xin Wu ◽  
Tong Wang ◽  
Di Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Prediction Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Prediction Problem ◽  
Bp Neural Networks ◽  
Advantages And Disadvantages

This paper focuses on the simulation and prediction problem of height of the crops, particularly the wheat, which plays a significantly important role in its yield, in different growing stages. Our model bases on the BP neural network and the ant colony algorithm. Both of these two algorithms has their own advantages and disadvantages. However, through observations, we find that their advantages and disadvantages seem to be complementary, by which we propose the combination algorithm. This combination algorithm can conquer the local optimum problem of the BP Neural Network, and could overcome the shortcomings of the weak local optimum searching capability of the ant colony algorithm. The experiments show that the our proposed algorithm can hopefully yield good simulation and prediction results of the height of wheat in different growing stages.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

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