Improved Elman neural network with ant colony algorithm and its applications in fault diagnosis

2010 ◽  
Author(s):  
Zheng Yao ◽  
Guohuan Lou ◽  
Qingxin Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Elman Neural Network

Application of Ant Colony Algorithms and Neural Networks in Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 3014-3017
Author(s):  
Jing Bo Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Weak Points ◽  
Diagnosis Method ◽  
Computational Simplicity ◽  
Minimum Points ◽  
Neural Network Technology

Neural network technology is widely applied due to its computational simplicity and versatility. But, this method has some weak points, for example, slow convergence, less accurate and easy to fall into local minimum points. Combined ant colony algorithm and neural network for fault diagnosis, it can overcome the limitations of a single fault diagnosis method. Ant colony neural network method is applied to gearbox fault diagnosis, the results show that the diagnosis with characteristics of high precision, strong scientific and practical wider.

ACO Trained ANN-Based Intelligent Fault Diagnosis

2010 ◽  
Vol 20-23 ◽  
pp. 141-146
Author(s):  
Hua Wang Shi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Nonlinear Function ◽  
Rolling Bearing ◽  
Ant Colony ◽  
Traditional Algorithm ◽  
Artificial Neural Network Ann ◽  
Intelligent Model

By use of the properties of ant colony algorithm and particle swarm optimization, this paper presents an application of an Ant Colony Optimization (ACO) algorithm and artificial neural network (ANN) to fault diagnosis. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behavior of real ants in nature searching for food. Neural network is used to express the nonlinear function between the input and output of the fault diagnosis of the rolling bearing. And ant colony optimization (ACO) algorithm is used to learn NN. The new algorithm has the merits of both ACO algorithm and neural network. It also provides a new way for the fault diagnosis through constructing the intelligent model by ant colony-neural network and overcomes the shortcomings of traditional algorithm.

The Research on Ant Colony Algorithm-Neural Networks Model in Fault Diagnosis of Asynchronous Motor

2012 ◽  
Vol 614-615 ◽  
pp. 1625-1628
Author(s):  
Jia Tang Cheng ◽  
Li Ai ◽  
Shao Kun Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Asynchronous Motor ◽  
Ant Colony ◽  
Minimum Problem ◽  
Threshold Values ◽  
Bp Network ◽  
The Mean ◽  
Local Minimum Problem

In order to improve the accuracy of fault diagnosis of asynchronous motor, neural network model combined with ant colony algorithm is presented. Taken the mean square error as objective function, then the weights and threshold values are optimized through multiple generation computation of ant colony, and the fault diagnosis is accomplished via the optimized neural network. The simulation results show that the algorithm is to overcome the slow convergence, easy to fall into local minimum problem of BP network, and achieve good diagnosis.

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