Testability analysis and behavioral testing of the Hopfield neural paradigm

Testability analysis and test pattern generation for neural architectures can be performed at a very high abstraction level on the computational paradigm. In this paper, we consider the case of Hopfield's networks, as the simplest example of networks with feedback loops. A behavioral error model based on finite-state machines (FSM's) is introduced. Conditions for controllability, observability and global testability are derived to verify errors excitation and propagation to outputs. The proposed behavioral test pattern generator creates the minimum length test sequence for any digital implementation.