期刊论文

Liu, Yan

英文

Neurocomputing

0925-2312

2018

311

270-278

10.1016/j.neucom.2018.05.075

This work is Supported by the National Natural Science Foundation of China (Nos. 61403056 and 11401185 ), Natural Science Foundation Guidance Project of Liaoning Province (No. 201602050), and Dalian Youth Science and Technology Star Project (No. 2017RQ129). This work is Supported by the National Natural Science Foundation of China (Nos. 61403056 and 11401185), Natural Science Foundation Guidance Project of Liaoning Province (No. 201602050), and Dalian Youth Science and Technology Star Project (No. 2017RQ129).

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This paper investigates the split complex gradient descent based neuro-fuzzy algorithm with self-adaptive momentum and L2 regularizer for training TSK (Takagi–Sugeno–Kang) fuzzy inference models. The major threat for disposing complex data with fuzzy system is contradiction of boundedness and analyticity in the complex domain, as expressed by Liouville's theorem. The proposed algorithm operates a couple of real-valued functions and splits the complex variables into real part and imaginary part. Dynamical momentum is included in the learning m...