Multi-directional Model Validity Tests for Nonlinear System Identification

New multi-directional model validation test procedures for a wide class of nonlinear modelling methods

Model Identification and Assessment Based on Model Predicted Output

Conventional system identification algorithms are based on the minimisation of the one step ahead prediction errors. In this study it is shown that one step ahead predictions do not always provide a good assessment of model quality. The model predicted output which can be considered as the long range prediction is suggested as an alternative criterion for model assessment. Based on this criterion a new system identification algorithm is developed

Rational Model Data Smoothers and Identification Algorithms.

This study presents a new algorithm for nonlinear rational model identification. The new algorithm consists of a two-step procedure: a nonlinear rational function smoother is initially designed and used to smooth the data, system identification is then performed based on the smoothed signal. By using the smoothed signal instead of the raw data, the severe noise problems, which arise in the rational model identification, are avoided. The new approach significantly simplifies the procedure for dynamic nonlinear rational model identification, compared with earlier estimators and provides unbiased estimates with the same degree of accuracy

Structure Detection for Nonlinear Rational Models Using Genetic Algorithms.

A new nonlinear rational model identification algorithm is introduced based on genetic algorithms. Compared with other rational model identification approaches, the new algorithm has two main advantages. First, this algorithm does not require a linear-in-the-parameters regression equation and as a consequence the severe noise problems induced by multiplying out the rational model are avoided. Second, the new algorithm provides near-optimal global parameter estimation. Unfortunately, this is balanced by an enormous computational load even when identifying models which consist of modest parameter sets. Simulated examples are included to illustrate that the new algorithm works well on simple simulated examples but can fail when applied in more realistic situations

Variable Selection in Nonlinear Systems Modelling

A new algorithm which preselects variables in nonlinear system models is introduced by converting the problem into a variable selection procedure for a set of linearised models. Based on this result an algorithm which consists of a cluster analysis linearisation sub-region division procedure, a linear subset selection routine usin an all possible regression algorithm and a genetic algorithm is developed. This algorithm can be applied to the modelling of nonlinear systems using a wide class of model forms including the nonlinear polynomial model, the nonlinear rational model, artificial neural networks and others. Numerical simulations are included to demonstrate the efficiency of the new algorithm

Algorithms for Minimal Model Structure Detection in Nonlinear Dynamic System Identification

The minimal model structure detection (MMSD) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidate terms would involve 2.43 x 10 (18) possible paths, it is shown that this can typically be reduced to 2 x 10 (3) by augmenting the orthogonal estimation algorithm with genetic search procedures. The MMSD algorithm provides the first practical solution for optimal structure detection in NARMAX modelling, training neural networks and fuzzy systems modelling. Based on the MMSD algorithm, a refined forward regression orthogonal (RFRO) algorithm is developed. The RFRO algorithm initially detects a parsimonious model structure using the forward regression orthogonal algorithm and then refines the model structure by applying the MMSD algorithm to the reduced model term set. The RFRO algorithm cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algorithm and can find a smaller model than the forward regression orthogonal algorithm

Magnetized gas in the smith high velocity cloud

We report the first detection of magnetic fields associated with the Smith High Velocity Cloud. We use a catalog of Faraday rotation measures toward extragalactic radio sources behind the Smith Cloud, new H I observations from the Robert C. Byrd Green Bank Telescope, and a spectroscopic map of Hα from the Wisconsin H-Alpha Mapper Northern Sky Survey. There are enhancements in rotation measure (RM) of =100 rad m-2 which are generally well correlated with decelerated Hα emission. We estimate a lower limit on the line-of-sight component of the field of =8 μG along a decelerated filament; this is a lower limit due to our assumptions about the geometry. No RM excess is evident in sightlines dominated by H I or Hα at the velocity of the Smith Cloud. The smooth Hα morphology of the emission at the Smith Cloud velocity suggests photoionization by the Galactic ionizing radiation field as the dominant ionization mechanism, while the filamentary morphology and high (=1 Rayleigh) Hα intensity of the lower-velocity magnetized ionized gas suggests an ionization process associated with shocks due to interaction with the Galactic interstellar medium. The presence of the magnetic field may contribute to the survival of high velocity clouds like the Smith Cloud as they move from the Galactic halo to the disk. We expect these data to provide a test for magnetohydrodynamic simulations of infalling gas

Role of microenvironment in the mixed Langmuir-Blodgett films

This paper reports the pi-A isotherms and spectroscopic characteristics of mixed Langmuir and Langmuir-Blodgett (LB) films of non-amphiphilic carbazole (CA) molecules mixed with polymethyl methacrylate (PMMA) and stearic acid (SA). pi-A isotherm studies of mixed monolayer and as well as also the collapse pressure study of isotherms definitely conclude that CA is incorporated into PMMA and SA matrices. However CA is stacked in the PMMA/SA chains and forms microcrystalline aggregates as is evidenced from the scanning electron micrograph picture. Nature of these aggregated species in the mixed LB films has been revealed by UV-Vis absorption and fluorescence spectroscopic studies. The presence of two different kinds of band systems in the fluorescence spectra of the mixed LB films have been observed. This may be due to the formation of low dimensional aggregates in the mixed LB films. Intensity distribution of different band system is highly sensitive to the microenvironment of two different matrices as well as also on the film thicknessComment: 11 pages, 5 figure

A Regularised Least Squares Algorithm for Nonlinear Rational Model Identification

A new regularised least squares estimation algorithm is derived for the estimation of nonlinear dynamic rational models. Theoretical analysis and numerical simulations demonstrate that the new algorithm provides improved estimates compared with previously developed rational model estimators

Effect of tensor couplings in a relativistic Hartree approach for finite nuclei

The relativistic Hartree approach describing the bound states of both nucleons and anti-nucleons in finite nuclei has been extended to include tensor couplings for the $\omega$- and $\rho$-meson. After readjusting the parameters of the model to the properties of spherical nuclei, the effect of tensor-coupling terms rises the spin-orbit force by a factor of 2, while a large effective nucleon mass $m^{*}/M_{N} \approx 0.8$ sustains. The overall nucleon spectra of shell-model states are improved evidently. The predicted anti-nucleon spectra in the vacuum are deepened about 20 -- 30 MeV.Comment: 31 pages, 4 postscript figures include