Modelling and identification of non-linear deterministic systems in the delta-domain

This paper provides a formulation for using the delta-operator in the modelling of non-linear systems. It is shown that a unique representation of a deterministic non-linear auto-regressive with exogenous input (NARX) model can be obtained for polynomial basis functions using the delta-operator and expressions are derived to convert between the shift- and delta- domain. A delta-NARX model is applied to the identification of a test problem (a Van-der-Pol oscillator): a comparison is made with the standard shift operator non-linear model and it is demonstrated that the delta-domain approach improves the numerical properties of structure detection, leads to a parsimonious description and provides a model that is closely linked to the continuous-time non-linear system in terms of both parameters and structure

Maximum-likelihood estimation of delta-domain model parameters from noisy output signals

Fast sampling is desirable to describe signal transmission through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast

Operator-Based Truncation Scheme Based on the Many-Body Fermion Density Matrix

In [S. A. Cheong and C. L. Henley, cond-mat/0206196 (2002)], we found that the many-particle eigenvalues and eigenstates of the many-body density matrix $\rho_B$ of a block of $B$ sites cut out from an infinite chain of noninteracting spinless fermions can all be constructed out of the one-particle eigenvalues and one-particle eigenstates respectively. In this paper we developed a statistical-mechanical analogy between the density matrix eigenstates and the many-body states of a system of noninteracting fermions. Each density matrix eigenstate corresponds to a particular set of occupation of single-particle pseudo-energy levels, and the density matrix eigenstate with the largest weight, having the structure of a Fermi sea ground state, unambiguously defines a pseudo-Fermi level. We then outlined the main ideas behind an operator-based truncation of the density matrix eigenstates, where single-particle pseudo-energy levels far away from the pseudo-Fermi level are removed as degrees of freedom. We report numerical evidence for scaling behaviours in the single-particle pseudo-energy spectrum for different block sizes $B$ and different filling fractions \nbar. With the aid of these scaling relations, which tells us that the block size $B$ plays the role of an inverse temperature in the statistical-mechanical description of the density matrix eigenstates and eigenvalues, we looked into the performance of our operator-based truncation scheme in minimizing the discarded density matrix weight and the error in calculating the dispersion relation for elementary excitations. This performance was compared against that of the traditional density matrix-based truncation scheme, as well as against a operator-based plane wave truncation scheme, and found to be very satisfactory.Comment: 22 pages in RevTeX4 format, 22 figures. Uses amsmath, amssymb, graphicx and mathrsfs package

Sparse Bayesian Nonlinear System Identification using Variational Inference

IEEE Bayesian nonlinear system identification for one of the major classes of dynamic model, the nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied to date. Markov chain Monte Carlo (MCMC) methods have been developed, which tend to be accurate but can also be slow to converge. In this contribution, we present a novel, computationally efficient solution to sparse Bayesian identification of the NARX model using variational inference, which is orders of magnitude faster than MCMC methods. A sparsity-inducing hyper-prior is used to solve the structure detection problem. Key results include: 1. successful demonstration of the method on low signal-to-noise ratio signals (down to 2dB); 2. successful benchmarking in terms of speed and accuracy against a number of other algorithms: Bayesian LASSO, reversible jump MCMC, forward regression orthogonalisation, LASSO and simulation error minimisation with pruning; 3. accurate identification of a real world system, an electroactive polymer; and 4. demonstration for the first time of numerically propagating the estimated nonlinear time-domain model parameter uncertainty into the frequency-domain

Creating a self-induced dark spontaneous-force optical trap for neutral atoms

This communication describes the observation of a new type of dark spontaneous-force optical trap (dark SPOT) obtained without the use of a mask blocking the central part of the repumper laser beam. We observe that loading a magneto-optical trap (MOT) from a continuous and intense flux of slowed atoms and by appropriately tuning the frequency of the repumper laser is possible to achieve basically the same effect of the dark SPOT, using a simpler apparatus. This work characterizes the new system through measurements of absorption and fluorescence imaging of the atomic cloud and presents a very simple model to explain the main features of our observations. We believe that this new approach may simplify the current experiments to produce quantum degenerated gases.Comment: 13 pages, 8 figures, Submitted to Optics Communications (30/10/2003), accepted for publication (Feb/2004

Topological effects at short antiferromagnetic Heisenberg chains

The manifestations of topological effects in finite antiferromagnetic Heisenberg chains is examined by density matrix renormalization group technique in this paper. We find that difference between integer and half-integer spin chains shows up in ground state energy per site when length of spin chain is longer than $\sim\xi$, where $\xi\sim\exp(\pi S)$ is a spin-spin correlation length, for spin magnitude S up to 5/2. For open chains with spin magnitudes $S=5/2$ to S=5, we verify that end states with fractional spin quantum numbers $S'$ exist and are visible even when the chain length is much smaller than the correlation length $\xi$. The end states manifest themselves in the structure of the low energy excitation spectrum.Comment: 4 pages, 6 figure

A Readout System for the STAR Time Projection Chamber

We describe the readout electronics for the STAR Time Projection Chamber. The system is made up of 136,608 channels of waveform digitizer, each sampling 512 time samples at 6-12 Mega-samples per second. The noise level is about 1000 electrons, and the dynamic range is 800:1, allowing for good energy loss ($dE/dx$) measurement for particles with energy losses up to 40 times minimum ionizing. The system is functioning well, with more than 99% of the channels working within specifications.Comment: 22 pages + 8 separate figures; 2 figures are .jpg photos to appear in Nuclear Instruments and Method

Numerical renormalization group study of the 1D t-J model

The one-dimensional (1D) $t-J$ model is investigated using the density matrix renormalization group (DMRG) method. We report for the first time a generalization of the DMRG method to the case of arbitrary band filling and prove a theorem with respect to the reduced density matrix that accelerates the numerical computation. Lastly, using the extended DMRG method, we present the ground state electron momentum distribution, spin and charge correlation functions. The $3k_F$ anomaly of the momentum distribution function first discussed by Ogata and Shiba is shown to disappear as $J$ increases. We also argue that there exists a density-independent $J_c$ beyond which the system becomes an electron solid.Comment: Wrong set of figures were put in the orginal submissio