<title>Real-world applications of computational intelligence in aerospace sensing and controls: sparse, noisy, and subjective data</title>

Abstract

Many real-world problems in aerospace sensing and controls can be approached by adding to traditional analytic techniques more information found in sparse, noisy and subjective data. Such data frequently plays a part in the modeling methodology. but formalizing its role can lead to better control and documentation ofthe system, extending its future. The combination of computational intelligence (CI) tools with the traditional system can provide techniques for appropriate generalization (or lack of it) from sparse data. Noise to be modeled or interpreted may not obviously follow a familiar distribution such as uniform, exponential. Gaussian. Raleigh, Poisson or Weibull. Many subjective decisions are made in implementing challenging data as one of the familiar distributions or in determining an appropriate empirical distribution. These problems can be successfully addressed by careful combination of artificial neural systems, fuzzy or soft systems and evolutionary systems. Recommended methodology is illustrated by examples from missile system guidance and control simulations. Expert interpretation of problem scenarios is recorded and timed to provide direction for project extensions and enhanced data visualization. Commercial applications ofthese methodologies to aerospace industry decision support systems and to biomedical control applications is discussed and illustrated.