An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technolo...An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller en- semble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-off to reduce the prediction error (the sum of bias2 and variance).展开更多
This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which...This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which is updated by the pilot symbols. The proposed approach can trace the variation of channel and it is computationally simple. Its performance is tested via simulations. Results show that it is comparable to that of ideal Minimum Mean-Square-Error （MMSE） method, especially at the low Signal to Noise Ratio （SNR）.展开更多
文摘An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller en- semble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-off to reduce the prediction error (the sum of bias2 and variance).
文摘This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which is updated by the pilot symbols. The proposed approach can trace the variation of channel and it is computationally simple. Its performance is tested via simulations. Results show that it is comparable to that of ideal Minimum Mean-Square-Error （MMSE） method, especially at the low Signal to Noise Ratio （SNR）.
