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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Csukas, B. Bankuti, G. |
| Copyright Year | 2004 |
| Description | Author affiliation: Inst. of Math. & Inf. Technol., Kaposvar Univ., Hungary (Csukas, B.; Bankuti, G.) |
| 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. |
| Starting Page | 1227 |
| Ending Page | 1232 |
| File Size | 426186 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780383591 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2004.1380118 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-07-25 |
| Publisher Place | Hungary |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Artificial neural networks Mathematical model Neural networks Integral equations Physics computing Intelligent networks Mathematics Information technology Collaborative tools Problem-solving |
| Content Type | Text |
| Resource Type | Article |
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