A SVM capable of identifying faults by off-line learning from a small sample data is introduced into a high-gain observer, which helps improve the state estimation accuracy and reconfigure the controller more precisely. (2) Considering that the frequency domain characteristics of the position signal...
but prior to this study it has not been shown that such non-invasive systems could achieve proficient multi-dimensional control of a robotic arm to reach and grasp an object in three-dimensional (3D) space. The concept of machine control via non-invasive EEG has been proposed in previous ...
Regression SVM, Databank building and pattern generation Having validated the DNN and gained confidence on the prediction, we further exploited the trained DNN to construct a databank that containing all the possible patterns and their corresponding responses. In this study, we were able to use ...
SVM outperformed the other data analysis methods, including PLS, in both the prediction accuracy for the active kinase-inhibitor combinations as manifested by P2and P2kinparameters (Tables1and2) and in the ability to distinguish interacting versus non-interacting kinase-inhibitor pairs as revealed by ...
Some examples of such algorithms include Self-Organizing (Kohonen) Maps, Multi-Dimensional Scaling, Isomap, and Locally-Linear Embedding. One example algorithm is Diffusion Maps, as described in further detail below. Further details on Diffusion Maps can be found in S. Lafon and A. B. Lee, ...
An eddy-current damper is applied considering its noncontact and linear properties. The state-variable modal decomposition method (SVMD) is applied to extract the modes from impact responses. Characteristics of the mode shapes and modal damping are examined for various values of the damping ...
(SVM) can be used. SVMs are a set of machine learning methods capable of performing non-linear regression and classification of numerical data [67,68]. For example, RNAz uses a regression SVM to compute single sequencezscores and a classification SVM to determine whether a multiple sequence ...
Linear layer: The input size of the linear layer is 3944 and the output size is 128. The output is activated using the ReLU function. Output layer: The output layer has an input size of 128 and output size of 2. The two-dimensional output vector represents the probabilities of a sample...
{-5}as thep-value threshold), education, age, and sex to be important factors, with slightly different orders. It demonstrates that those factors are important and robust in predicting CI. Additionally, simple linear models, including logistic regression and SVM with linear kernel, are consistent...
The SVM classification uses default parameters, and only kernel functions are adjusted. Considering the linear and the nonlinear classification, aside from the linear kernel function, some other kernel functions are adjusted to achieve optimal classification peformance53. The classification results from ...