The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is an asymmetric variation of the minority game with linear payoff functions. For each game, simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is ess...

A novel estimate for the line-to-ground capacitance that accurately predicts the pull-in instability parameters for narrow electrostatically actuated microbeams is proposed. Parameters in the proposed formula are obtained by least square fitting data from a fully converged numerical solution with the method of moments. For a narrow microbeam, it is shown that the new formula significantly impro...

The Order-Value Optimization (OVO) problem is a generalization of the classical Minimax problem. Instead of the maximum of a set functions, the functional value that ranks in the p−th place is minimized. The problem seeks the application to (non-pessimistic) decision making and to model fitting in the presence of (perhaps systematic) outliers. A Cauchy-type method is introduced that solves the ...

Am&-The paper presents a simple derivation of the method of fitting nonlinear algebraic models where all variables are subject to error and improves the numerical efficiency of the algorithm. Including a known procedure for equilibrating balance equations and factorizing the weighting matrix, the classical Gauss-Marquardt method of estimating parameters in nonlinear models is shown to handle al...

The classical curve-fitting problem to relate two variables, x and y, deals with polynomials. Generally, this is solved by the least squares method (LS), where minimization function considers vertical errors from data points fitting curve. Another total (TLS), which takes into account in both y variables. A further orthogonal distances (OD), minimizes sum of In work, we develop OD for polynomia...

Contents Preface v Chapter 1. Introduction and historic overview 1 1.1. Classical regression 1 1.2. Errors-in-variables (EIV) model 3 1.3. Geometric fit 5 1.4. Solving a general EIV problem 8 1.5. Non-linear nature of the 'linear' EIV 11 1.6. Statistical properties of the orthogonal fit 13 1.7. Relation to total least squares (TLS) 15 1.8. Nonlinear models 16 1.9. Notation and preliminaries 17 ...

We propose a novel linear dimensionality reduction algorithm, namely Locally Regressive Projections (LRP). To capture the local discriminative structure, for each data point, a local patch consisting of this point and its neighbors is constructed. LRP assumes that the low dimensional representations of points in each patch can be well estimated by a locally fitted regression function. Specifica...

Fully hydrated stacks of DOPC lipid bilayer membranes generate large diffuse x-ray scattering that corrupts the Bragg peak intensities that are used in conventional biophysical structural analysis, but the diffuse scattering actually contains more information. Using an efficient algorithm for fitting extensive regions of diffuse data to classical smectic liquid crystalline theory we first obtai...

Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a f...