Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems
Kim, Jaesoo; Kasabov, Nikola
In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme composed of two phases: the phase of rule generation from data, and the phase of rule tuning by using the error backpropagation learning scheme for a neural fuzzy system. In order to illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamics are carried out. The proposed method can be applied to on-line incremental adaptive leaning for the purpose of prediction and control of non-linear dynamical systems.
Publisher: University of Otago
Series number: 99/05
Keywords: neuro-fuzzy systems; neural networks; fuzzy logic; parameter and structure learning; knowledge acquisition; adaptation; time series
Research Type: Discussion Paper
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