SVM for Sensor Fusion-a Comparison with Multilayer Perceptron Networks

Sensor fusion is a method of integrating signals from multiple sources. This paper investigated the possibility of using a new universal approximator: support vector machines (SVMs), as the sensor fusion architecture for the accuracy measurement and estimation of lumber moisture content in the wood drying process. The result of comparative analysis with multilayer perceptron was given. The training algorithm of MLP may be trapped in a local minimum and has a difficult task to determine the best architecture. SVM based on structural risk minimization can overcome these disadvantages. Experimental results show that the SVM performs as well as the optimal multilayer perceptron (MLP)