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RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center...
One of the major problems in target recognition is that targets may be changed with translation, rotation, scale and intensity. A numerals recognition model based on PCNN (pulse-coupled neural networks) and FPF (fractional-power filter) is proposed in this paper, which use inherent ability of PCNN to extract feature and capability of FPF allowing invariance to be built into can recognize numerals...
Jet engine gas path fault diagnosis is not only important in modern condition-based maintenance of aircraft engines, but also a challenging classification problem. Exploring more advanced classification techniques for achieving improved classification performance for gas path fault diagnosis, therefore, has been increasingly active in recent years in PHM community. To that end, in this paper, we apply...
Channel mismatch, including amplitude and phase mismatch, has severely affected the performance of space-time adaptive processing (STAP). In order to ascertain the specification of the effects on STAP, theory analysis of relation between clutter covariance matrix (CCM) with and without channel mismatch is done based on the model provided by literature, and the effect to signal is also analyzed. In...
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