Title :
On performance measures of artificial neural networks trained by structural learning algorithms
Author :
Kozma, R. ; Kitamura, M. ; Malinowski, A. ; Zurada, J.M.
Author_Institution :
Dept. of Nucl. Eng., Tohoku Univ., Sendai, Japan
Abstract :
Structural learning in multi layer, feedforward neural networks was studied using M. Ishikawa´s (1994) modified backpropagation algorithm with forgetting of the connection weights. The proper choice of forgetting constant was investigated previously but no generally accepted method has been established yet. The generalization rate of the trained network is analyzed as a possible means of selecting optimum model parameters. The results are illustrated using R.A. Fisher´s (1936) IRIS data and anomaly detection in time series
Keywords :
backpropagation; feedforward neural nets; generalisation (artificial intelligence); multilayer perceptrons; software performance evaluation; IRIS data; anomaly detection; artificial neural networks; connection weights; forgetting constant; generalization rate; modified backpropagation algorithm; multi layer feedforward neural networks; optimum model parameters; performance measures; structural learning algorithms; time series; Artificial neural networks; Computer networks; Error analysis; Iris; Multi-layer neural network; Neural networks; Signal analysis; Signal processing; Testing; Time series analysis;
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-7174-2
DOI :
10.1109/ANNES.1995.499430
