The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a novel method of applying auto-regressive(AR) model and random forests to fault diagnosis of power electronic circuit. AR model is used to extract the features of the sample data, realize optimum compressed of fault sample data, simplify the data structure in fault diagnosis, enhance classify speed and precision. By simulating fault status of power electronic circuit, this paper...
This paper presents a new method of applying auto-regressive (AR) model and hidden Markov model (HMM) to fault diagnosis of power electronic circuit. AR coefficients is used as Exacting the features of the states of the circuit firstly .Then fault modes are trained and recognized by hidden Markov model. Finally, a three-phase SCR rectifier circuit with inductive load is used as an example to illustrate...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.