Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets

A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems