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In this paper, the concept of ensemble learning is adopted and applied to modeling multi-target regression problems with high-dimensional feature spaces and a small number of instances. A novel neural network ensemble (NNE) model is introduced, called Boosted-NNE based on notions from boosting, subspace projection methods and the negative correlation learning algorithm (NCL). Rather than using an...
In this paper, Constrained Least Mean Square (CLMS) algorithm for narrowband adaptive beamforming for tracking mobile user in a 2D urban environment has been used. This algorithm is capable of efficiently adapting according to the environment and able to permanently maintain the chosen frequency response in the look direction while minimizing the output power of the array. The capability of the smart...
LMS filtering can be viewed as solving the Wiener-Hopf equation iteratively using the Richardson's iteration with an identity matrix for a preconditioner. The ideal preconditioner in this situation is the inverse of the autocorrelation matrix of the input signal. This is why LMS is the optimal adaptive filter for white input signals. In situations where the input signal is not white one can improve...
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