We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling h...
Rebecca Hutchinson, Tom M. Mitchell, Indrayana Rus...
Abstract. In this article we describe a semantic extension of event-driven process chains, with which it is possible to specify the semantics of individual model elements as it is ...
This paper explores how the dynamics of complex biological processes can be modelled and simulated as an organisation of multiple agents. This modelling perspective identifies org...
This paper presents a comprehensive formulation of a linearized state space process model for a generic two-reactant-two-product reactive distillation system. The development of t...
: A genetic algorithm (GA) based recurrent fuzzy neural network modeling method for dynamic nonlinear chemical process is presented. The dynamic recurrent fuzzy neural network (RFN...