|Title||Evolving predictive neural models for complex processes|
|Publication Type||Presentazione a Congresso|
|Year of Publication||2007|
|Authors||De Felice, Matteo, Annunziato M., Bertini I., Panzieri S., and Pizzuti S.|
|Conference Name||CITSA 2007 - Int. Conference on Cybernetics and Information Technologies, Systems and Applications and CCCT 2007 - Int. Conference on Computing, Communications and Control Technologies, Proceedings|
|Keywords||Artificial life, Complex Processes, Cybernetics, Dynamical systems, Evolutionary method, Evolutionary neural network, Information technology, Large scale systems, Model identification, Models, Neural networks, Prediction horizon, Topology, Training algorithms, Unknown disturbance|
In this work we present a study on Artificial Neural Networks (ANN) performance with different topologies and training algorithms in order to develop a model of a dynamical system, focusing on the effect of unknown inputs (disturbance). Therefore, we study the ANN performance in model identification and prediction by training them using traditional gradient based and evolutionary methods. Tests have been made with different prediction horizons on two experimentations: without disturbance and with a pulse train unknown disturbance. Results show that without disturbances the performance of gradient trained ANN is slightly better than that of evolutionary trained ANN. The situation is different when in the presence of disturbance: gradient trained ANN performance gets much worse compared to evolutionary ANN.