When the fuzzy factors is in the sample or noise around the classification surface which is the decisive factor for the summary of support vector machines (SVM), the operation results of SVM will be greatly affected. In this case, the noise must be effectively removed to minimize the impact on the sample. Here, the method of “one class to the rest class” is used to establish fault classifier, and a decision algorithm combining binary tree with fuzzy support vector machine is proposed. In this algorithm, the noise points near the classification surface in the samples are removed by SDWFCM (Spacial distance weighted fuzzy C-Means) method firstly. Then, based on the fuzzy feature representation method, the fuzzy support vector machine is improved by combining the fuzzy membership degree determination method and the basis of the threshold determination after fuzzy programming. The support vector machine can effectively process the composite samples of mixed fuzzy samples, in order to accomplish the fault diagnosis of a certain type of equipment.
Aiming at the problem of UAV track evaluation, the main evaluation indexes related to it are analyzed from three aspects (performance, mission and defense) that affect the track quality. The target weight is determined by the entropy weight method, and the track evaluation model based on TOPSIS is established. Finally, the availability of the model is proved by an example.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.