Ant Colony Neural Network Research Articles

Studies of Synchronous Rotor Gear Monitoring Technique Based on Ant Colony Neural Network

In this paper, the nonlinear mapping relationship between characteristic parameters of failures and failure types is realized by using neural network through extracting characteristic variables of failures during operation of the gear. Aiming at the problems of neutral network such as slow convergence speed and existence of local minima, the neural network is optimized and the ant colony neural network is established by using the ant colony algorithm to realize rapid and accurate determination of failure status of a gear from characteristic parameters of failures. In addition, validity of the established model is verified through experiments.

An Image Recognition Method Based on Ant Colony Optimization Neural Network Using in Imaging Fuze

Target image recognition is an important issue in the information processing of imaging fuse system. In the paper, the main frame is proposed which can solve the problem of target image recognition and many computer simulation experiments are carried out. A recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithm and converges fast. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition, so it is very useful in improving the effective destroying ability of the missile.

Sensor Compensation Based on Adaptive Ant Colony Neural Networks

In order to improve work stability and measurement accuracy of drilling inclinometer, and overcome the poor stability of Elman networks and lower compensation precision of genetic Elman neural networks, we combined ant colony algorithm and neural networks, using the Adaptive Ant Colony Algorithm that its pheromone evaporation factorand pheromone update strategy adjust adaptively to optimize Elman neural network weights and thresholds, and applied it to drilling inclinometer sensor compensation. Simulation results show that the compensation effect of adaptive ant colony Elman neural networks is better than that of Elman networks and genetic Elman networks, the compensation accuracy is 10-8.

An Approach to Traffic Volume Forecasting Based on Ant Colony Neural Network

Combining the ant colony algorithm (ACA) and the neural network (NN), the present paper puts forward an approach to traffic volume forecasting based on the ant colony neural network. The approach employs the ACA with mutation features to train the weights of an artificial neural network (ANN), thus it is characterized by large mapping capacity of the NN, and by rapidity, global convergence, and heuristic learning of the ACA. Simulation results of an actual example indicate that this approach can improve the forecasting accuracy, hence its validity.

Research & Realization on Neural Network for Forecast of AM Automatic Logistics Information-System

This paper gives research on Neural Network for AM Automatic Logistics information System’s Modeling, Simulation and algorithm, contrasted to traditional methods of forecast. In order to raise the design efficiency and get the most excellent design effect, this paper combined Ant Colony Optimization (ACO) algorithm and neural networks, which based on ACO algorithm and the implementing framework of ACO. It gives the basic theory and steps; which proved research on Neural Network for AM Automatic Logistics information-System has good ability of non-linear function’s approach and latent feedback’s dynamic data processing, study online time short, parameter convergence swift, self-adapt ability good, can study and adapt to severity uncertainly dynamic system’s tract, forecast precision is high.

ACO Trained ANN-Based Intelligent Fault Diagnosis

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.