Study On Boosting Algorithm Of Neural Network Ensemble

There is shortcoming of error malignant accumulation in AdaBoostalgorithms. As the wrong sample weight rises to certain proportion, thevicious circle will appear and always continue. In order to avoid thisvicious circle, this study of AdaBoost algorithm includes the following:(1) For there being too much emphasis on the difficult samples inAdaBoost algorithm weights modifying policy, this paper presentsERstd—AdaBoost algorithms to modify the weights based on thecontroversial degree. This algorithm decides the size of the weightadjusted according to the dispute size and the classified error orcorrection result. This difference in the adjustment to sample weights willinhibit the accumulation of difficult sample weights round by round incertain degree. Because improving the generalization performance of theindividual classifiers under the premise of without losing the differences,thus the generalization performance of the integrated network isimproved. (2) For there being overfitting in AdaBoost algorithm, this paperpresents ABSD algorithms to modify the weights based on sampledistribution. ABSD algorithm can reduce too much emphasis on thedifficult samples, and greatly reduce the overfitting, and can effectivelyimprove the generalization performance of the integrated network.(3) This paper studied the generalization performance of integratednetwork in reverse weight distribution strategy, generalizationperformance of individual classifiers and the differences degree ofintegrated network, and proposed the improved algorithms (IB+) inreverse weight distribution strategy. The generalization performance ofthe improved algorithm in reverse weight distribution strategy wassignificantly better than of algorithm in forward weight distributionstrategy.(4) The feature extraction method based on time-domain signalwaveform was proposed for on-line fault diagnosis of rolling bearings.Based on AHP analysis methods to determine the different weights of thethree measurement indicators (the minimum Euclidean distance, the sumof Euclidean distance, the Euclidean distance variance), this papercompleted the comprehensive evaluation of the feature extractionmethods in same dimension, and given a quantitative result ofcomprehensive evaluation. Comprehensive evaluation method can notonly verify the separability and effectiveness of the extraction method, but also provide the measure appraisal method for the different methodchoices and feature selection and optimization.(5) Using improved reverse algorithm IB+integrated by ABSD, thetraining result of conventional statistical features, the optimizedwaveform vertical features and horizontal features have verified that thegeneralization performance of IB+algorithm is better than otheralgorithms, and also verified that the classification ability of waveformfeatures is better than of conventional statistical features.