Failure and power utilization system models of differential equations by polynomial neural networks

Reliability modeling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. Complex manufacturing systems often require a high level of reliability from the incoming electricity supply. Modern industrial time power quality monitoring systems can be used for the pre-fault load alarming. Neural networks can successfully model and predict the failure frame of critical electronic systems and power utilization in power plants described only a few input quantities. Differential polynomial neural network is a new type of neural network, which constructs and substitutes an unknown general sum partial differential equation with a total sum of fractional polynomial terms. The system model describes partial relative derivative dependent changes of some input combinations of variables. This type of non-linear regression is based on trained generalized data relations decomposed by partial low order polynomials of 2-input variables. Experimental results indicate that the proposed method is efficient.