scholarly journals Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianyong Liu ◽  
Huaixiao Wang ◽  
Yangyang Sun ◽  
Chengqun Fu ◽  
Jie Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Search Space ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Quantum Genetic Algorithm ◽  
Binary Coding ◽  
Optimal Value ◽  
Neural Network Algorithm

The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

scholarly journals English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dan Wu ◽  
Yuanjun Shen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Good Effect ◽  
Data Mining Algorithm ◽  
Main Function ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 4400-4404 ◽  
Author(s):  
Chun Mei Zhu ◽  
Chang Peng Yan ◽  
Xiao Li Xu ◽  
Guo Xin Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Mixed Model ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Speed Up ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

The Coal Slurry Pipeline Pressure Prediction Research Based on Quantum Genetic BP Neural Network

2013 ◽  
Vol 336-338 ◽  
pp. 722-727
Author(s):  
Xue Cun Yang ◽  
Yuan Bin Hou ◽  
Ling Hong Kong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Thermal Power ◽  
Coal Slurry ◽  
Cylinder Pressure ◽  
Master Cylinder ◽  
Quantum Genetic Algorithm ◽  
Model Based

According to the coal slime pipeline blockage problem of coal gangue thermal power plant, after the analysis of the actual scene, it is sure that thick slurry pump master cylinder pressure prediction is the necessary premise of blockage prediction. The thick slurry pump master cylinder pressure prediction model is proposed, which is based on QGA-BP (Quantum genetic Algorithm BP neural network). The simulation results show that the prediction model based on QGA-BP can be used to predict the paste pump outlet pressure, and the relative error is less than 8%, which can satisfy the engineering requirement .And compared with prediction model based on GA-BP(the genetic Algorithm BP neural network), The QGA-BP prediction model is better than GA-BP model in prediction accuracy and optimization time.

Data-Driven Approach for Equipment Reliability Prediction Using Neural Network

2011 ◽  
Vol 411 ◽  
pp. 563-566 ◽  
Author(s):  
Feng Ding ◽  
Xing Ben Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Optimization Theory ◽  
Descent Method ◽  
Numerical Control ◽  
Data Driven ◽  
Gradient Descent Method ◽  
Training Algorithm ◽  
Cnc Machine

BP neural network based data-driven method is proposed to predict reliability in this paper. The BP neural network prediction using Gradient Descent Method (GDM), Additional Momentum Gradient Descent Method (AMGDM) and Levenberg-Marquardt Method(L-M) based on numerical optimization theory of training algorithm are compared with different neuron number. The proposed approach is validated via age data collected from computer numerical control (CNC) machine tool in the field. The results from the proposed method show that perfect predicting performance is achieved under considering selecting suitable number of the hidden neurons and training algorithm. Remarks are outlined regarding the fact that BP neural network based on data-driven method is feasible, effective and adequate predicting accuracy can be obtained.

Intrusion Detection Engineering Management Based on Optimization of BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1228-1232
Author(s):  
Jian Hua Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Fitness Function ◽  
Engineering Management ◽  
Data Set ◽  
Binary Coding ◽  
Optimized Model ◽  
Input Variables ◽  
The Individual

In order to short the modelling time of BP neural network, this paper designs a kind of genetic algorithm to optimize it. By encoding the individual components, initializing the number of populations, and designing proper fitness function, a binary coding genetic algorithm is provided for BP neural network. And it is used to optimize input variables of BP neural network and reduce its dimension. The experiment is carried out based on KDD Cup 99 data set. The results show that the optimized model has shorter modelling time.

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