標題: An on-line self-constructing neural fuzzy inference network and its applications
作者: Juang, CF
Lin, CT
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: equalizer;noisy speech recognition;projection-based correlation measure;similarity measure;TSK fuzzy rule
公開日期: 1-二月-1998
摘要: A self constructing neural fuzzy inference network (SONFIN) with on-line learning ability is proposed in this paper, The SONFIN is inherently a modified Takagi-Sugeno-Kang (TSK) type fuzzy rule-based model possessing neural network's learning ability, There are no rules initially in the SONFIN, They are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification, In the structure identification of the precondition part, the input space is partitioned in a flexible way according to a aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially, Afterwards, some additional significant terms (input variables) selected via a projection-based correlation measure for each rule will be added to the consequent part (forming a linear equation of input variables) incrementally as learning proceeds, The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN, In the parameter identification, the consequent parameters are tuned optimally by either least mean squares (LMS) or recursive least squares (RLS) algorithms and the precondition parameters are tuned by backpropagation algorithm, Both the structure and parameter identification are done simultaneously to form a fast learning scheme, which is another feature of the SONFIN, Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved, Proper linear transformations are also learned dynamically in the parameter identification phase of the SONFIN, To demonstrate the capability of the proposed SONFIN, simulations in different areas including control, communication, and signal processing are done, Effectiveness of the SONFIN is verified from these simulations.
URI: http://dx.doi.org/10.1109/91.660805
http://hdl.handle.net/11536/32809
ISSN: 1063-6706
DOI: 10.1109/91.660805
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 6
Issue: 1
起始頁: 12
結束頁: 32
顯示於類別:期刊論文


文件中的檔案:

  1. 000072774800002.pdf