This study is mainly focusing on the problem of spacecraft close-range proximity with obstacle avoidance in presence complex shape. A novel Gaussian mixture model–based nonsingular terminal sliding mode control (GMM-NTSMC) proposed. achieved by developing GMM-based potential function a switching surface NTSMC. It theoretically proved that closed-loop system globally stable. The main contributio...

Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP....

In this paper, methods of Gaussian Mixture Model (GMM) are presented for both silence/voiced/voiceless segmentation and tone decision in Mandarin continuous speech recognition system. GMM has been used for silence/voiced/voiceless segmentation before, but the feature parameters can be modified to improve both accuracy and speed. As a popular method in pattern recognition, GMM is first proposed ...

We 1 develop some theoreti al results for a robust similarity measure named generalized minmax (GMM). This similarity has dire t appli ations in ma hine learning as a positive de nite kernel and an be e iently omputed via probabilisti hashing. Owing to the dis rete nature, the hashed values an also be used for e ient near neighbor sear h. We prove the theoreti al limit of GMM and the onsisten y...

We propose the “generalized min-max” (GMM) kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named “generalized consistent weighted sampling” (GCWS) which linearizes this (nonlinear) kernel. A natural competitor of GMM is the radial basis function (RBF) kernel, whose correspond...