Neural networks and genetic algorithms for combinatorial optimization of sensor data fusion
A. DeCegama, Jeffrey E. Smith · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The sensor data fusion problem can be formulated as a combinatorial optimization problem. Simulated annealing is a technique based on an analogy with the physical process of annealing which can find solutions to such problems arbitrarily close to an optimum. However, the computational effort involved can be prohibitive especially to obtain high quality solutions of large problems. Parallel processing offers the capability to provide the required computational power for real time performance in sensor data fusion applications by taking advantage of the massive parallelism and distributed representations of neural networks. Several types of neural networks, e.g., Gaussian, Boltzmann, and Cauchy machines, have been proposed to implement the technique of simulated annealing in parallel according to different cooling schedules but such neural networks have not previously been analyzed in terms of their capabilities for the specific problem of sensor data fusion. This paper presents the results of research conducted in order to evaluate the neural network approach to the combinatorial optimization problem intrinsic in real time sensor data fusion. A comparison with other advanced technique, i.e., genetic algorithms, is also being investigated.