Stochastic learning methods for dynamic neural networks: simulated and real-data comparisons

Abstract

In the paper some stochastic methods for dynamic neural network training are presented and compared. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of fault diagnosis system to generate residuals. Classical optimisation techniques, based on back propagation idea, suffer from many well-known drawbacks. Two stochastic algorithms are tested as training algorithms to overcome these difficulties. Efficiency of proposed learning methods is checked on two examples: modelling of an unknown linear dynamic system basing on simulated data and modelling of the actuator behaviour in the first section of the evaporation station in the Sugar Factory, Lublin using real data measurements. In these two significant examples, the stochastic learning algorithms are extensively compared from many different perspectives.