Name
Abstract | ||
|---|---|---|
A multilayer recurrent fuzzy neural network (MRFNN) is proposed for dynamic system modeling in this paper. The proposed MRFNN has six layers combined with T-S fuzzy model. The recurrent structures are formed by local feedback connections in the membership layer and the rule layer. With these feedbacks, the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well. The parameters of MRFNN are learned by modified chaotic search (CS) and least square estimation (LSE) simultaneously, where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly. Simulation results of chaos system identification show the proposed approach is effective for dynamic system modeling with high accuracy. And then the proposed approach is applied to a batch reactor modeling. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CIS.2007.124 | CIS |
Keywords | Field | DocType |
least squares approximation,fuzzy neural network,inductors,batch reactor,fuzzy sets,dynamic system,modeling,neurofeedback,system identification,fuzzy set | Least squares,Fuzzy model,Neuro-fuzzy,Control theory,Computer science,Fuzzy set,Artificial intelligence,Systems modeling,Chaotic search,Artificial neural network,System identification,Machine learning | Conference |
Volume | Issue | ISBN |
null | null | 0-7695-3072-9 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||