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This chapter outlines a typical workflow for micraorray data analysis. It aims at explaining the background of the methods as this is necessary for deciding upon a specific numerical method to use and for understanding and interpreting the outcomes of the analyses. We focus on error handling, various steps during preprocessing (clipping, imputing missing values, normalization, and transformation of data), statistic tests for variable selection and the use of multiple hypothesis testing procedures, various metrics and clustering algorithms for hierarchical clustering, principles, and results from principal components analysis and discriminant analysis, partitioning, selforganizing map, K-nearest neighbor classifier, and the use of a neural network and a support vector machine for classification. [ABSTRACT FROM AUTHOR]

Algorithms for the identification of continuous time nonlinear plants operating in open-loop and in closed-loop are presented. An adjustable output error type predictor is used in open-loop operation. An adjustable output error type predictor parametrized in terms of the existing controller and the estimated plant model is used in closed-loop operation. The algorithms are derived from stability considerations in the absence of noise and assuming that the plant model is in the model set. Some convergence results based on passivity concepts are presented. Subsequently the algorithms are analyzed in the presence of noise and when the plant model is not in the model set. Examples illustrate the use of the various algorithms. [ABSTRACT FROM AUTHOR]

Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter uncertainty set, from which a corresponding uncertainty set can be constructed in the space of transfer functions. We display the role of the experimental conditions used for validation on the shape of this validated set, and we connect a measure of the size of this set to the stability margin of a controller designed from the nominal model. This allows one to check stability robustness for the validated model set and to propose guidelines for validation design. [ABSTRACT FROM AUTHOR]