34 results about "Support vector method" patented technology

Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel.

The present invention provides a voltage sag waveform point detection method based on the space vector method, which specifically includes the following steps: Step 1. Obtain the three-phase voltage instantaneous value waveform of the voltage sag to be detected. Step 2. Extract the basic features of known waveforms. Step 3. Construct a space vector through the three-phase voltage vector, and calculate the rate of change of the radius of the space vector. Step 4. Through the rate of change of the radius of the space vector, find the position of the sampling point where the waveform point is located. Step 5. Calculate the waveform point by combining the angle between the space vector x(t) and the real axis and the position of the sampling point obtained. The invention synthesizes space vectors by three-phase voltages, detects waveform points according to different characteristics of the space vectors when no sag occurs and when sag occurs, and solves errors in existing algorithms in principle.

The invention discloses a magnetic resonance parallel imaging method of a multi-support vector model, which belongs to the field of magnetic resonance parallel imaging. The method comprises completely sampling an intermediate region in a K space, dividing into a training set and a testing set, performing accelerated sampling on the other regions to obtain samples as a prediction set, and performing normalization processing on the data in the sets; dividing the training set into a plurality of training subsets, and selecting different parameters to train each training subset by using a support vector to obtain different combined weighting function models; testing the combined weighting functions on the testing set, and selecting a plurality of optimal submodels; and predicting the prediction set by using the optimal submodels, taking an average value as a value of uncollected points, performing reverse normalization processing, and converting K space data into an image. The parallel imaging method has good generalization and small overall reconstruction error by using weighting functions fitted by the support vector.

The invention provides a single-bit spatial spectrum estimation method based on a support vector machine and relates to the field of support vectors in spatial spectrum estimation field and artificial intelligence in array signal processing. Problems of complicated calculation and quite low precision in conditions of single-bit extreme quantization and super-large scale antenna arrays are solved. The single-bit spatial spectrum estimation in a large-scale antenna array is modeled into a classification problem in artificial intelligence and the spatial spectrum of wave signals is solved by use of the support vector machine method. Compared with the traditional algorithm, the provided estimation method is advantageous in that estimation precision of the spatial spectrum is improved; the structure of a receiving machine is simplified; and angles of multiple signal resources can be simultaneously estimated. The estimation method is used for estimation of the spatial spectrums.