A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines Export

@article{citeulike:2219173, abstract = {This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.}, author = {Sun, Zhan-Li and Au, Kin-Fan and Choi, Tsan-Ming}, booktitle = {Systems, Man, and Cybernetics, Part B, IEEE Transactions on}, citeulike-article-id = {2219173}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TSMCB.2007.901375}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4305290}, doi = {10.1109/TSMCB.2007.901375}, journal = {Systems, Man, and Cybernetics, Part B, IEEE Transactions on}, keywords = {fuzzy, learning, reinforcement}, number = {5}, pages = {1321--1331}, posted-at = {2008-01-11 14:48:23}, priority = {4}, title = {A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines}, url = {http://dx.doi.org/10.1109/TSMCB.2007.901375}, volume = {37}, year = {2007} }

TY - JOUR ID - citeulike:2219173 L3 - citeulike-article-id:2219173 N2 - This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods. IS - 5 JF - Systems, Man, and Cybernetics, Part B, IEEE Transactions on EP - 1331 TI - A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines VL - 37 T2 - Systems, Man, and Cybernetics, Part B, IEEE Transactions on SP - 1321 KW - fuzzy KW - learning KW - reinforcement AU - Sun, Zhan-Li AU - Au, Kin-Fan AU - Choi, Tsan-Ming PY - 2007/// UR - http://dx.doi.org/10.1109/TSMCB.2007.901375 ER -