Santos R., Rupp M., Bonzi S., Fileti A., 2013, Comparison Between Multilayer Feedforward Neural Networks and a Radial Basis Function Network to Detect and Locate Leaks in Pipelines Transporting Gas, Chemical Engineering Transactions, 32, 1375-1380.
An artificial neural network is a technique of artificial intelligence that has the ability to learn from experiences, improving its performance by adapting to the changes in the environment. The main advantages of neural networks are: the possibility of efficient manipulation of large amounts of data and its ability to generalize results. Considering the great potential of this technique, this paper aims to establish a comparison between Multilayer Feedforward - a Multilayer Perceptron network (MLP) with feedforward learning - and a Radial Basis Function Network (RBF). The RBF and MLP networks are usually employed in the same kind of applications (nonlinear mapping approximation and pattern recognition), however their internal calculation structures are different. A comparison was made by using experimental data from a microphone installed inside a galvanized iron pipeline of 60 m length, under various operating conditions. The signal from the microphone coupled to a data acquisition board in a microcomputer was decomposed in different frequency noises. The dynamics of these noises in time were used as inputs to the neural models to locate and determine the magnitude of the leaks (model outputs). The results obtained from the test sets, with leaks caused intentionally, showed that the two neural structures were able to detect and locate leaks in pipes. Nevertheless, the Multilayer Perceptron network showed a slightly better performance.
