Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Neural network design
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Fault diagnosis of pneumatic systems with artificial neural network algorithms
Expert Systems with Applications: An International Journal
One-class support vector machines-an application in machine fault detection and classification
Computers and Industrial Engineering
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
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Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications.