Software Planned Learning and Recognition Based on the Sequence Learning and NARX Memory Model of Neural Network

In traditional way, software plans are represented explicitly by some semantic schemas. However, semantic contents, constrains and relations of plans are hard for explicit presentation. Besides, it is a heavy and error-prone work to build such a library of plans. Algorithms of recognition of such plans demand exact matching by which semantic denotation is obvious itself. We thus present a novel approach of applying neural network in the presentation and recognition of plans via asymmetric Hebbian plasticity and non-linear auto-regressive with exogenous inputs (NARX) to learn and recognize plans. Semantics of plans are represented implicitly and error-tolerant. The recognition procedure is also error-tolerant because it tends to match fuzzily like human. Models and relevant limitations are illustrated and analyzed in this article