document: An improved self-organizing CPN-based fuzzy system with adaptive back-propagation algorithm This paper describes an improved self-organizing CPN-based -LRB- Counter-Propagation Network -RRB- fuzzy system . Two self-organizing algorithms IUSOCPN and ISSOCPN , being unsupervised and supervised respectively , are introduced . The idea is to construct the neural-fuzzy system with a two-phase hybrid learning algorithm , which utilizes a CPN-based nearest-neighbor clustering scheme for both structure learning and initial parameters setting , and a gradient descent method with adaptive learning rate for fine tuning the parameters . The obtained network can be used in the same way as a CPN to model and control dynamic systems , while it has a faster learning speed than the original back-propagation algorithm . The comparative results on the examples suggest that the method is fairly efficient in terms of simple structure , fast learning speed , and relatively high modeling accuracy
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extractive_keyphrases: hybrid learning gradient descent structure learning initial parameters setting
abstractive_keyphrases: self-organizing fuzzy system counter-propagation network neural-fuzzy system back-propagation learning scheme