A Neuromorphic Controller for a Distillation Column

This paper investigates the design of a neural network based controller to control the concentration of the overhead and bottom product in the model of a distillation column. Satisfactory computer simulation results of this approach are obtained

Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks

Electrocardiogram (ECG) signal has been widely used in cardiac pathology to detect heart disease. In this paper, wavelet neural network (WNN) is studied for ECG signal modeling and noise reduction. WNN combines the multi-resolution nature of wavelets and the adaptive learning ability of artificial neural networks, and is trained by a hybrid algorithm that includes the adaptive diversity learning particle swarm optimization (ADLPSO) and the gradient descent optimization. Computer simulation results demonstrate this proposed approach can successfully model the ECG signal and remove high-frequency noise

Traffic Signal Optimization Using Ant Colony Algorithm

Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users’ delays. Ant Colony Optimization (ACO) is a meta-heuristic algorithm based on the behavior of ant colonies searching for food. ACO has successfully been employed to solve many complicated combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic networks. This research investigates the application of the ant colony algorithm to minimize user delay at traffic intersections. Various ACO algorithms are discussed and a rolling horizon approach is also employed to achieve real-time adaptive control. Computer simulation results show that this new approach outperforms conventional fully actuated control, especially under the condition of high traffic demand

Modeling of a Gyro-stabilized Helicopter Camera System Using Artificial Neural Networks

On-board gimbal systems for camera stabilization in helicopters are typically based on linear models. Such models, however, are inaccurate due to system nonlinearities and complexities. As an alternative approach, artificial neural networks can provide a more accurate model of the gimbal system based on their non-linear mapping and generalization capabilities. This paper investigates the applications of artificial neural networks to model the inertial characteristics (on the azimuth axis) of the inner gimbal in a gyro-stabilized multi-gimbal system. The neural network is trained with time-domain data obtained from gyro rate sensors of an actual camera system. The network performance is evaluated and compared with measurement data and a traditional model. Computer simulation results show the neural network model fits well with the measurement data and significantly outperforms the traditional model