Simulation and Modelling Knowledge-Mining Architectures Using Recurrent Hybrid Nets

Abstract

Hybrid recurrent nets combine arithmetic and integrator elements to form nodes for modeling the complex behaviour of intelligent systems with dynamics. Given the behaviour pattern of such nodes it is required to determine the values of their causal parameters. The architecture of this knowledge mining process consists of two stages: time derivatives of the trajectory are determined first, followed by the parameters. Hybrid recurrent nets of first order are employed to compute derivatives continuously as the behaviour is monitored. A further layer of arithmetic and hybrid nets is then used to track the values of the causal parameters of the knowledge mining model. Applications to signal processing are used to illustrate the techniques. The theoretical foundations of this knowledge mining process is presented in the first part of the chapter, where the application of dynamical systems theory is extended to abstract systems to illustrate its broad relevance to any system including biological and non physical processes. It models the complexity of systems in terms of observability and controllability.