This paper presents a new approach, which exploits the recently developed Dynamic Fuzzy Neural Networks (DFNN) learning algorithm. The DFNN is based on extended Radial Basis Function (RBF) neural networks, which are functionally equivalent to Takagi-Sugeno-Kang (TSK) fuzzy systems. The algorithm comprises 4 parts: (1) Criteria of rules generation; (2) Allocation of premise parameters; (3) Determination of consequent parameters and (4) Pruning technology. The salient characteristics of the approach are: (1) A hierarchical on-line self-organizing learning paradigm is employed so that not only parameters can be adjusted, but also the determination of structure can be self-adaptive without partitioning the input space a priori; (2) Fast learning speed can be achieved so that the system can be implemented in real time. The application of the proposed approach is demonstrated in application to a demanding, highly nonlinear, missile control design task. Scheduling on instantaneous incidence (a rapidly varying quantity) is well known to lead to considerable difficulties with classical gain-scheduling methods. It is shown that the methods proposed here can, however, be used to successfully design an effective intelligent controller.
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