New color management model for digital camera based on immune genetic algorithm and neural network

2007 ◽  
Author(s):  
Xinwu Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Digital Camera ◽  
Management Model ◽  
Color Management ◽  
Immune Genetic Algorithm

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.

scholarly journals Sales Forecasting Model Based on BP Neural Network Optimized by Improved Immune Genetic Algorithm

2021 ◽  
Author(s):  
Kunyu Cao ◽  
Yongdang Chen ◽  
Xinxin Song ◽  
Shan Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Forecasting Model ◽  
Antibody Concentration ◽  
Immune Genetic Algorithm ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract A new sales forecasting model based on an Improved Immune Genetic Algorithm (IIGA), IIGA that optimizes the BPNN (IIGA-BP) has been proposed. The IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operator and mutation operator, which effectively improved the convergence ability and optimization anility of IIGA. And IIGA can optimize the BPNN’s initial weights and threshold and improve the randomness of network parameters as well as the drawbacks that lead to output instability of BPNN and easiness into local minimum value. It taking the past records of sales figures of a certain steel enterprise as an example, utilizing the Matlab to construct the BP neural network, Immune Genetic Algorithm that optimizes the BPNN (IGA-BP), IGA-BP neural network, and IIGA-BP neural network prediction models for simulation and comparative analysis. The experiment demonstrates that IIGA-BP neural network prediction model possessing a higher prediction accuracy and more stable prediction effects.

Prediction of Dynamic Deformation Monitoring Based on IGA Artificial Neural Network Model

2012 ◽  
Vol 204-208 ◽  
pp. 4760-4765
Author(s):  
Yu Zhu ◽  
Qing Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Artificial Neural Network Model ◽  
Dynamic Deformation ◽  
Deformation Monitoring ◽  
Immune Genetic Algorithm ◽  
Forecasting Accuracy ◽  
Density Regulation ◽  
Biological Immune System

Dynamic deformation data analysis and prediction is a complex systematic project. Aimed at the shortcoming of the traditional prediction model, a method to design the BP neural network based on Immune Genetic Algorithm(IGA) was proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system were introduced into IGA based on genetic algorithm. The proposed algorithm overcame the problems of GA on search efficiency, individual diversity and prematur, and enhanced the convergent performance effectively. The results show that the BP neural network designed by IGA have better performance in convergent speed and global convergence, and the forecasting accuracy is improved, which illustrates IGA-BP neural network has certain of value on dynamic deformation monitoring forecasting.

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.

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