An Approach to Intelligent Traction Control Using Regression Networks and Anomaly Detection

Figures & data

FIGURE 1 Basic electronic differential model.

FIGURE 2 Overall flow of operation.

FIGURE 3 PRN structure.

FIGURE 4 Flow of PRN.

TABLE 1 No. of Training Data vs Error and Timec

No. of Training Data	Error (%)	Training Time (secs)
36	1	0.7

FIGURE 5 Test error and training error vs. training examples.

FIGURE 6 Training time vs. number of examples.

FIGURE 7 Training and test error vs. number of polynomial features.

TABLE 2 Error vs. No. of Polynomial Features

No. of Polynomial Features	Error %	Training Time (secs)
6	0.6	0.7

FIGURE 8 Training time vs. number of polynomial features.

FIGURE 9 Training and test set error with varying lambda.

FIGURE 10 Actual front wheel movement vs. PRN prediction.

FIGURE 11 Anomaly detection points.

FIGURE 12 Anomalous values.