An Exploration Of The Relationship Between Changing Inequality Of Individual,Household And Regional Inequality In Australian Cities

The changes in neighbourhood income inequality noted in Gregory and Hunter (1995) indicate that significant changes in the nature of Australian cities have occurred since the mid-1970s. This paper seeks to establish that the regional dimension of income inequality is becoming a more important component of overall inequality. Furthermore, low status and high status areas are becoming increasingly homogenous with more low income households living together in low status neighbourhoods and higher income households living elsewhere. The inadequacies of the regional income data currently available means that it is difficult to explore changes in social welfare in any detail.     

Neural Networks For Matrix Scaling And S-Procedure Problem Solving

Linear matrix inequalities (LMIs) play a very important role in postmodern control by providing a framework that unifies many concepts. While many papers have addressed the issue for solving LMIs using sequentially numerical algorithms, few have examined solving related LMIs using neural network processing. The aim of this paper is to show the potential of using recurrent neural networks to solve these problems. Two representative LMI problems are considered. First, the problem of scaling a matrix to reduce its norm, which appears often in robust control applications, is considered. Second, the approach is extended to solve the S-procedure problem, which is closely related to the stability of particular nonlinear systems. Illustrative examples are provided to demonstrate use of the proposed approach.     

Optimal memoryless regulator of large‐scale systems with time delays in the interconnections and the states via decentralized control

A method to construct optimal memoryless regulators of large‐scale systems with time delays in the interconnections and the states is proposed. Each feedback gain of decentralized control laws is obtained directly from solutions of simultaneous linear matrix inequalities. First, a sufficient condition for the stability of the overall closed‐ loop system is presented. Then it is extended so that the overall system and each subsystem remain stable, even if all or some of the interconnections between subsystems are cut. Lastly, a simplification of the implementation is discussed in the perspective of feedback loop reduction. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 177(4): 42–50, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21191     

Ultimate boundedness control of linear systems with band-bounded nonlinear actuators and additive measurement noise

For linear systems driven by band-bounded nonlinear actuators, a set of linear matrix inequality (LMI) based sufficient conditions are derived for finding state feedback controllers which assure ultimate boundedness of state trajectories. Besides actuator nonlinearity, it is assumed that additive noise exists when state variables are measured for feedback. The purpose is to minimize the ultimate boundedness region while tolerating noise of the largest magnitude. When a state feedback controller is determined for a given system by solving the LMI conditions or by any other means, a less conservative LMI condition is given for further examination of the resultant ultimate boundedness region and tolerable noise magnitude.     

Hermite matrix in Lagrange basis for scaling static output feedback polynomial matrix inequalities

Using Hermite's formulation of polynomial stability conditions, static output feedback (SOF) controller design can be formulated as a polynomial matrix inequality (PMI), a (generally nonconvex) nonlinear semidefinite programming problem that can be solved (locally) with PENNON, an implementation of a penalty and augmented Lagrangian method. Typically, Hermite SOF PMI problems are badly scaled and experiments reveal that this has a negative impact on the overall performance of the solver. In this note we recall the algebraic interpretation of Hermite's quadratic form as a particular Bézoutian and we use results on polynomial interpolation to express the Hermite PMI in a Lagrange polynomial basis, as an alternative to the conventional power basis. Numerical experiments on benchmark problem instances show the improvement brought by the approach, in terms of problem scaling, number of iterations and convergence behaviour of PENNON.     

The Generalized Jackknife Statistic

This paper shows how tabulated exact factors for prediction intervals for all k future observations, under the assumption of normality, readily provide conservative factors for some values of k beyond the range of the tabulations.     

Improved results on delay-interval-dependent robust stability criteria for uncertain neutral-type systems with time-varying delays

In this paper, we consider the problem of delay-interval-dependent robust stability and stabilization of a class of linear uncertain neutral-type systems with time-varying delay. By constructing a candidate Lyapunov–Krasovskii functional (LKF), that takes into account the delay-range information appropriately, less conservative robust stability criteria are proposed in terms of linear matrix inequalities (LMIs) to compute the maximum allowable upper bounds (MAUB) for the delay-interval within which the uncertain neutral-type system under consideration remains asymptotically stable. The verifiable stabilizability conditions and memoryless state feedback control design are stated. Finally, numerical examples are also designated to demonstrate the effectiveness and reduced conservatism of the developed results.     

Sequential slippage tests using Robbins-Lai's invariant confidence sequences

The paper deals with two kinds of location-slippage testing problems.The alternative hypothesis says that there is a single slippage in one kind of problems,and that there are multiple slippages in the other kind. In a location slippage model, by using Robbins' inequality, Robbins-Lai's invariant confidence sequences for slippage are constructed for both of the problems and sequential slippage tests using those confidence sequences are proposed.     

Comparison of methods to extract an asymmetry parameter from data

Several methods to extract an asymmetry parameter in an event distribution function are discussed and compared in terms of statistical precision and applicability. These methods are: simple counting rate asymmetries, event weighting procedures and the unbinned extended maximum likelihood method. It is known that weighting methods reach the same figure of merit (FOM) as the likelihood method in the limit of vanishing asymmetries. This article presents an improved weighting procedure reaching the FOM of the likelihood method for arbitrary asymmetries. Cases where the maximum likelihood method is not applicable are also discussed.