Generic bi-layered net of the "functional nodes" in process modeling | IEEE Conference Publication | IEEE Xplore

Generic bi-layered net of the "functional nodes" in process modeling


Abstract:

There is a tendency to integrate the 'a priori' knowledge in neural networks in the form of "functional nodes". This paper presents a novel method for the appropriate des...Show More

Abstract:

There is a tendency to integrate the 'a priori' knowledge in neural networks in the form of "functional nodes". This paper presents a novel method for the appropriate description of the whole process model in the form of a net, consisting of two basic kinds of "functional nodes". The generic bi-layered net (GBN) model provides a common framework for the simulation of the hybrid (continuous and discrete, quantitative and qualitative) balance-based and rule-based processes. The common features of the process models are represented by a bi-layered net that also determines the network (ring) structures of the influence routes and of the flux routes, as well as the Gantt chart view of the time-variant process. Artificial neural networks seem to be a useful collaborating tool of the GBN in the model based problem solving. The structure of the GBN models can be homomorphic or isomorphic with the recurrent neural networks.
Date of Conference: 25-29 July 2004
Date Added to IEEE Xplore: 17 January 2005
Print ISBN:0-7803-8359-1
Print ISSN: 1098-7576
Conference Location: Budapest, Hungary

References

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