Comparison of PSO-Based Optimized Feature Computation for Automated Configuration of Multi-sensor Systems

Abstract

The design of intelligent sensor systems requires sophisticated methods from conventional signal processing and computational intelligence. Currently, a significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer. Clearly, an automatic method for auto-configuration of sensor systems would be salient. In this paper, we contribute to the optimization of the feature computation step in the overall system design, investigating multi-level thresholding (MLT) and Gaussian windowing. Our goals are to compare these two feature computation methods and two evolutionary optimization techniques, i.e., genetic algorithm (GA) and particle swarm optimization (PSO). To compare with previous research work gas sensor benchmark data is used. In the comparison of GA and PSO the latter method provided superior results of 100% recognition in generalization for thresholding, which proved to be more powerful method.