| Summary: | 博士 === 國防大學理工學院 === 國防科學研究所 === 100 === Due to immense potential applications, wireless sensor networks (WSNs) have attracted research interests in recent years, including remote environmental monitoring, data fusion, sensing (temperature, pressure, speed) and military applications. This dissertation applies the fuzzy logic and neural network technologies to a monitored area which deployed miniature wireless sensor nodes. With the advantages of inherent accuracy and simplicity, the fuzzy logic and neural network technologies manifests the effectiveness on the environmental monitoring and control applications of wireless sensor networks.
First, we apply the fuzzy technology to control the air-conditioning strength and blade angle of a car conditioner to equalize the comfortable temperature in the front- and rear-seat areas. The wireless nodes equipped with temperature sensor are installed to gather temperature information and then transmit this information to the central control terminal which executes the fuzzy inference control logic. The experiments show that the fuzzy technology would greatly improve the response for the automotive control and smart computation in the wireless sensor network systems.
And then we develop a novel fuzzy logic algorithm to the remote environmental monitoring applications. Through a simple and effective fuzzy logic algorithm, every interesting node in the monitored area can be effectively calculated. This novel algorithm manifests their simplicity and accuracy and its performance characterized by root mean square error is better than the one with the standard Mamadni fuzzy logic method.
Our study focuses on two particular neural network models, back-propagation network (BPN) and general regression neural network (GRNN) for the temperature prediction in a monitored factory. The prediction accuracy of these two models is evaluated by practical monitored data. We found that the model based on GRNN can accelerate the learning speed and rapidly converge to the optimal regression surface with large number of data sets. With the simulation results, we can show that the model based on GRNN effectively improve the predictability of the one based on BPN.
Finally, we combine the genetic algorithm (GA) and the radial basis function (RBF) neural network in study of event detection for factory monitoring. As we know, the center of RBF, the width of RBF and output weight of RBF have a great influence on the performance of RBF neural network. In this study, we apply genetic algorithm to determine these parameters to improve the performance of the event detection. The experiments indicate that the GA-RBF algorithm is better than the traditional BPN and RBF neural network algorithms in both speed and precise of convergence. In this work, we find a responsive and effective algorithm in the WSN applications by integrating fuzzy theory and neural network technology. The combination of fuzzy theory and neural network technology should be a powerful strategy for the various WSN applications.
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