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

Abstract

ABSTRACT Many real-world problems in aerospace sensing and controls can be approached by adding to traditional analytictechniques more information found in sparse, noisy and subjective data. Such data frequently plays a part in the modelingmethodology. but formalizing its role can lead to better control and documentation ofthe system, extending its future. Thecombination of computational intelligence (CI) tools with the traditional system can provide techniques for appropriategeneralization (or lack of it) from sparse data. Noise to be modeled or interpreted may not obviously follow a familiardistribution such as uniform, exponential. Gaussian. Raleigh, Poisson or Weibull. Many subjective decisions are made inimplementing 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 andevolutionary systems. Recommended methodology is illustrated by examples from missile system guidance and controlsimulations. Expert interpretation of problem scenarios is recorded and timed to provide direction for project extensionsand enhanced data visualization. Commercial applications ofthese methodologies to aerospace industry decision supportsystems and to biomedical control applications is discussed and illustrated.Keywords: computational intelligence, missile systems, guidance and control. artificial neural networks1. INTRODUCTION1.1 Statement of the problemMany real-world problems in aerospace sensing and controls can be approached by adding to traditional analytictechniques more information found in sparse, noisy and subjective data. Such data frequently plays a part in modelingmethodology, but formalizing its role can lead to better control and documentation ofthe system, extending its future.1.2 Objectives and performance measures: goals and valuesBeginning a project with concurrent engineering principles in mind helps to establish meaningful and realisticgoals and assign values to different outcomes. Recording this information in project documentation is a standard practice.This produces material which is difficult to track and update. Often creative hunches and fixes are incorporated into thesystem without reaching the official documentation. The advent ofuser-friendly tools for fuzzy systems development andevolutionary I