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Model

A simplified version of something complex used, for example, to analyze and solve problems

 

Modified Sequential Least Squares System Identification

A challenge in real time system identification is the tradeoff between algorithm stability and rapid adaptation to parameter changes. For reconfigurable flight control, this is especially important due to long periods of quiescence, coupled with the possibility of sudden unforeseen failures. To address these twin challenges, Barron Associates developed the MSLS parameter identification algorithm. Building on recursive least squares (RLS), MSLS incorporates additional constraints that take advantage of a priori information and effectively adjusts the size of the data-window to account for the information content in the regressor.

Reference: Ward, D.G, R.L. Barron, M.P. Carley, and T.J. Curtis, "Real-time parameter identification for self-designing flight control," Proc. Nat'l Aerospace and Electronics Conf. (NAECON), Dayton, OH, May 1994.

Statistical Model Validation

Mathematical modeling and computer simulation have become indispensable tools in scientific, engineering, policy, and economic decision-making by government, industry, and other organizations. Barron Associates' Stochastic Model Validation (SMV) technology concerns the use of nonparametric (distribution-free) statistical techniques for validating multivariate stochastic simulation models. The validation approach is based on testing for homogeneity between a population of empirical data (observed data) and a population generated by one or more stochastic simulation models. Multivariate techniques are necessary to reveal whether a simulation model correctly captures dependencies among multiple response variables. Nonparametric approaches obviate the need for normality assumptions concerning the distributions of empirically-observed and model-generated data; in practice, such distributional assumptions are often not appropriate. Barron Associates' SMV approach is novel in that it enables validation across multiple treatments, where empirical and model data are collected under a variety of independent test conditions that are of operational interest, or which reflect the available observation data. Integrating non-homogeneous data into one overall statistical test substantially increases the power of the test relative to that of testing each treatment separately and avoids the pitfall of increased probability of significant findings due to chance alone.

Structure-Learning Neural Networks

Barron Associates' neural network modelling software suite, GNOSIS, can be used to find the best feedforward model that transforms observable variables into one or more estimated dependent variables. GNOSIS postulates a simple model, then incrementally adds complexity until a "just-sufficient" complexity level is achieved. In other words, GNOSIS determines the optimal trade-off between model complexity and model accuracy when determining the structure of the model. A proper balance ensures the best performance on unseen data by neither underfitting nor overfitting the data. Barron Associates has demonstrated the efficacy of the GNOSIS structure-learning technique in a number of fields.

Advanced Nonlinear Simulations

Barron Associates works with and develops in-house simulations of a number of advanced systems ranging from tailless aircraft to shipboard power distribution systems. As part of these efforts, Barron Associates is also developing tools to speed-up nonlinear simulations, to automatically update simulations given experimental data, and to visualize the results of these simulations.