OPT-RNN-DBSVM: OPTimal Recurrent Neural Network and Density-Based Support Vector Machine

Abstract

When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.

Monitoring the Wear Trend in Wind Turbines by Tracking the Fourier Vibration Spectrum and Base Density Support Vector Machine

“…This distance is [21,22]. The Mahalanobis distance is the smaller unit that takes into account the correlation of the data set and does not depend on the measurement scale [23,24]. The population variance is calculated with a variance-convariance matrix [24].…”

Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines

“…The cov −1 represents the inverse covariance matrix. This distance is explained in [33,34]. The Mahalanobis distance takes into account the correlation of the dataset and does not depend on the measurement scale [34][35][36].…”

“…This distance is explained in [33,34]. The Mahalanobis distance takes into account the correlation of the dataset and does not depend on the measurement scale [34][35][36]. The population variance is calculated with a variance-covariance matrix [35].…”

Monitoring the Wear Trend in Wind Turbines by Tracking the Fourier Vibration Spectrum and Base Density Support Vector Machine

“…The cov -1 represents the inverse covariance matrix. This distance is [33,34]. The Mahalanobis distance takes into account the correlation of the data set and does not depend on the measurement scale [34][35][36].…”

“…This distance is [33,34]. The Mahalanobis distance takes into account the correlation of the data set and does not depend on the measurement scale [34][35][36]. The population variance is calculated with a varianceconvariance matrix [35].…”