Application of Neural Network Based on the Immune Genetic Algorithm in Failure Diagnosis

2013 ◽  
Vol 706-708 ◽  
pp. 650-653
Author(s):  
Li Li Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Algorithm ◽  
Failure Diagnosis ◽  
Learning Ability ◽  
Feed Forward Neural Network ◽  
Immune Genetic Algorithm ◽  
The Neural Network ◽  
Simulation Results ◽  
Searching Ability

This paper designs the multilayer feed-forward neural network based on the immune genetic algorithm to solve the problem that BP algorithm is prone to get the local minimum in the failure diagnosis system. It is of both the learning ability and robustness of the neural network, as well as the strong global random searching ability of the immune genetic algorithm. The simulation results indicate the neural network can fulfill failure diagnosis of the complicated production better.

An Optimizing Design Approach for the Fiber Manufacturing Based on the Immune Genetic Algorithm-Optimized Neural Network

2011 ◽  
Vol 267 ◽  
pp. 19-24
Author(s):  
Hui Zhong Zhu ◽  
Yong Sheng Ding ◽  
Xiao Liang ◽  
Kuang Rong Hao ◽  
Hua Ping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Immune Genetic Algorithm ◽  
System A ◽  
The Neural Network ◽  
Proposed Model ◽  
Optimizing Design ◽  
Spinning Process ◽  
Fiber Manufacturing ◽  
Network Component

A novel neural network-based approach with immune genetic algorithm is proposed to conduct the optimizing design for the industrial filament manufacturing system. A new model is proposed in this paper to acquire better filament quality during such process. The proposed model was a combination of two components, namely, a traditional neural network which is used to simulate and an immune genetic algorithm-based part which is to improve the performance of the neural network component. Simulation results demonstrate that the proposed method can efficiently demonstrate the spinning process of filament and conduct the prediction of the filament quality with the production parameters as input data. Meanwhile, the proposed method enjoys faster speed and more precise accuracy, compared with traditional methods.

Application of Neural Network With New Hybrid Algorithm in Volcanic Rocks Seismic Prediction

2018 ◽  
Vol 12 (4) ◽  
pp. 55-68
Author(s):  
Yanchao Liu ◽  
Limei Yan ◽  
Jianjun Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Oil And Gas ◽  
Hybrid Genetic Algorithm ◽  
Volcanic Rocks ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Optimal Value ◽  
Global Optimal

This article has studied the application design and implementation of neural network with new hybrid algorithm in volcanic rocks prediction. It is considered that the convergence rate of EBP algorithm is slow, and the local minimum value can be obtained by EBP algorithm, and the approximation of global optimal value can be obtained by EBP algorithm. Therefore, genetic algorithm and EBP algorithm are proposed. The weight of the multilayer feed-forward neural network is determined by using the genetic BP algorithm. The new hybrid algorithm is applied to the neural network and volcanic oil and gas identification and compared with the traditional BP neural network. In contrast, using the new hybrid genetic algorithm to calculate the neural network is very small; you can quickly get the global optimal value.

Study on Assessment of Enterprise Intellectual Capital Based on the Genetic Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 237-240
Author(s):  
Tian Ying Jiang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intellectual Capital ◽  
Bp Algorithm ◽  
Practical Applications ◽  
The Neural Network ◽  
Genetic Neural Network ◽  
Optimized Model

Compared to the neural network BP algorithm, the optimized model of genetic neural network based on the genetic algorithm has a more close assessed result to the expected one and smaller relative mistakes. Practical applications show that the new assess way of enterprise intellectual capital is rational and accessible, and it provides as an important tool to enterprise for intellectual capital decision.

scholarly journals An unsupervised learning neural network for planning UAV full-area reconnaissance path

2021 ◽  
Vol 39 (1) ◽  
pp. 77-84
Author(s):  
Bo Li ◽  
Zhipeng Yang ◽  
Zhuoran Jia ◽  
Hao Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Unsupervised Learning ◽  
Uncertain Information ◽  
Evaluation Indexes ◽  
Environment Model ◽  
Offline Learning ◽  
The Neural Network ◽  
Training Samples ◽  
Simulation Results

To plan a UAV's full-area reconnaissance path under uncertain information conditions, an unsupervised learning neural network based on the genetic algorithm is proposed. Firstly, the environment model, the UAV model and evaluation indexes are presented, and the neural network model for planning the UAV's full-area reconnaissance path is established. Because it is difficult to obtain the training samples for planning the UAV's full-area reconnaissance path, the genetic algorithm is used to optimize the unsupervised learning neural network parameters. Compared with the traditional methods, the evaluation indexes constructed in this paper do not need to specify UAV maneuver rules. The offline learning method proposed in the paper has excellent transfer performances. The simulation results show that the UAV based on the unsupervised learning neural network can plan effective full-area reconnaissance paths in the unknown environments and complete full-area reconnaissance missions.

Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm

2013 ◽  
Vol 823 ◽  
pp. 335-339 ◽  
Author(s):  
Yin Ping Chen ◽  
Hong Xia Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Random Search ◽  
Bp Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Initial States ◽  
Searching Ability

This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.

scholarly journals Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

2019 ◽  
Vol 38 ◽  
pp. 117-124
Author(s):  
Guang Hu ◽  
Zhi Cao ◽  
Michael Hopkins ◽  
Conor Hayes ◽  
Mark Daly ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The Neural Network

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

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