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|Title:||Learning control using fuzzified self-organizing radial basis function network|
|Authors:||Nie, Junhong |
|Source:||Nie, Junhong,Linkens, D.A. (1993-11). Learning control using fuzzified self-organizing radial basis function network. IEEE Transactions on Fuzzy Systems 1 (4) : 280-287. ScholarBank@NUS Repository. https://doi.org/10.1109/91.251928|
|Abstract:||This note describes an approach to integrating fuzzy reasoning systems with radial basis function (RBF) networks and shows how the integrated network can be employed as a multivariable self-organizing and self-learning fuzzy controller. In particular, by drawing some equivalence between a simplified fuzzy control algorithm (SFCA) and a RBF network, we conclude that the RBF network can be interpreted in the context of fuzzy systems and can be naturally fuzzified into a class of more general networks, referred to as FBFN, with a variety of basis functions (not necessarily globally radial) synthesized from each dimension by fuzzy logical operators. On the other hand, as a result of natural generalization from RBF to SFCA, we claim that the fuzzy system like RBF is capable of universal approximation. Next, the FBFN is used as a multivariable rule-based controller but with an assumption that no rule-base exists, leading to a challenging problem of how to construct such as rule-base directly from the control environment. We propose a simple and systematic approach to performing this task by using a fuzzified competitive self-organizing scheme and incorporating an iterative learning control algorithm into the system. We have applied the approach to a problem of multivariable blood pressure control with a FBFN-based controller having six inputs and two outputs, representing a complicated control structure.|
|Source Title:||IEEE Transactions on Fuzzy Systems|
|Appears in Collections:||Staff Publications|
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