Comments on "Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties"

This note points out that the time complexity of the main multiple-surface sliding control (MSSC) algorithm in Huang and Chen [Huang, A. C. & Chen, Y. C. (2004). Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties. Automatica, 40(11), 1939-1945] is O(2^n). Here, we propose a simplified recursive design MSSC algorithm with time complexity O(n), and, using mathematical induction, we show that this algorithm agrees with this MSSC law.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

Adaptive fast finite time control of a class of uncertain nonlinear systems

This paper concerns the adaptive fast finite time control of a class of nonlinear uncertain systems of which the upper bounds of the system uncertainties are unknown. By using the fast non-smooth control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite time stability of the closed-loop system. It is proved that the control input is bounded, and a simulation example is given to illustrate the effectiveness of the theoretical results.

On performance of transform domain adaptive filters with Markov-2 inputs

In this paper, the analysis for the performance of the discrete Fourier transform LMS adaptive filter (DFT-LMS) and the discrete cosine transform LMS adaptive filter (DCT-LMS) for the Markov-2 inputs is presented. To improve the convergence property of the least mean squares (LMS) adaptive filter, the DFT-LMS and DCT-LMS preprocess the inputs with the fixed orthogonal transforms and power normalization. We derive the asymptotic results for the eigenvalues and eigenvalue distributions of the preprocessed input autocorrelation matrices with DFT-LMS and DCT-LMS for Markov-2 inputs. These results explicitly show the superior decorrelation property of DCT-LMS over that of DFT-LMS, and also provide the upper bounds for the eigenvalue spreads of the finite-length DFT-LMS and DCT-LMS adaptive filters. Simulation results are demonstrated to support the analytic results.

The adaptive significance of ontogenetic colour change in a tropical python

Ontogenetic colour change is typically associated with changes in size, vulnerability or habitat, but assessment of its functional significance requires quantification of the colour signals from the receivers' perspective. The tropical python, Morelia viridis, is an ideal species to establish the functional significance of ontogenetic colour change. Neonates hatch either yellow or red and both the morphs change to green with age. Here, we show that colour change from red or yellow to green provides camouflage from visually oriented avian predators in the different habitats used by juveniles and adults. This reflects changes in foraging behaviour and vulnerability as individuals mature and provides a rare demonstration of the adaptive value of ontogenetic colour change.

Family caregivers of the mentally ill and adaptive coping

The issue investigated in this thesis concerned the adaptive coping strategies that caregivers of the mentally ill adopt at different stages of encounter with their family member’s illness. Specifically, family caregivers’ responses to the illness were investigated within the parameters of the Spaniol and Zipple (1994) 4-stage model of the evolution of caregivers’ responses to mental illness. The accuracy of the model’s representation of the experience of caregivers across all kinship relationships to the care-recipient was evaluated. Spaniol and Zipple proposed four stages which they termed (1) Discovery/Denial, (2) Recognition/Acceptance, (3) Coping and (4) Personal/Political Advocacy. The first stage is characterised by persistent denial of mental illness and seeking answers from multiple sources. The second stage involves caregivers’ expectations of professionals providing answers when the illness is recognised. At this stage caregivers experience guilt, embarrassment and blame. The cyclical nature of the illness impedes acceptance and caregivers experience a deep sense of loss and crisis of meaning as they gradually accept the reality of the situation. In the third stage coping replaces grieving and the issues encountered include loss of faith in professionals, disruption to family life and recurrent crises. Belief in family expertise grows and the focus of coping changes. The fourth stage proposes that caregivers become more assertive, self-blame decreases and the focus is upon changing the system. New meanings and values are integrated. This study found that the model did not accurately describe the experience of all caregivers. Caregiver did not deny mental illness and adaptive coping occurred throughout all stages. Coping evolved as the issues encountered changed and was independent of resolution of grief. The issues encountered were more extensive than the model proposed and differed according to kinship relationship to the care recipient. The ways in which adaptive coping evolved were identified, as were the issues and their accompanying responses. Caregivers coped by adaptively responding to the requirements of care provision, maintaining a sense of self worth and generating positive effect.

Firms as adaptive organizations : the case of Australian trading banks

The conventional accounting notion of ‘going concern’ — that a firm will continue its business operations in the same manner indefinitely — has underpinned accounting practice for over one hundred years. This idea has provided a rationale for spreading costs over accounting periods and for deferring costs as assets in balance sheets. An alternative idea that is widely regarded as reliable in the literatures of economics and deliberate action is that firms continually adapt to changes in market and economic conditions. That is economic behaviour. The implications of that view of a firm for accounting have been systematically explored by Chambers (1966). While not examining those particular implications, many other accounting theorists have been critical of the conventional accounting idea of 'going concern' and of its impact on accounting practice. The two notions of ‘going concern’ - as static or adaptive enterprises - are examined by referring to the business operations of the four major Australian trading banks over the period 1983-1991. Banks were selected because they are commonly thought to be particularly ‘conservative’ organizations. The period 1983—1991 was chosen because it covers the era of deregulation of the Australian financial system. The evidence adduced by this study indicates that the Australian trading banks have continually adapted their organizational structures and business operations in the light of changes in technology, markets for financial services, government policies and domestic and global economic conditions. Illustrations of adaptive behaviour by banks ate drawn from their normal operating procedures such as the provision of products and services, loan services, acquisitions, sale of property, non-core banking operations and international banking. It is argued on analytical grounds that the cost basis of accounting does not yield financial statements that provide factual and up-to-date information about the financial capacity of firms to pay their debts and to continue trading generally; that is, to be going concerns. At any time, those financial capacities are determined by the amount of money commanded by a firm, including the money's worth of its assets, and by its level of debt. It is concluded on empirical grounds that the Australian trading banks, at least, are adaptive entities.

Delivering sustainability through the adaptive reuse of commercial buildings : the Melbourne CBD challenge

The City of Melbourne is aiming to be carbon neutral by 2020 (Arup 2008) and have set a target of adapting twelve hundred commercial buildings to incorporate sustainability initiatives to reduce greenhouse gas emissions from the sector (AECOM 2008). In order to meet this target the City of Melbourne is taking a proactive approach to establish strategies to deliver sustainability in the built environment within the 2020 timeframe. With regards to upgrading and building maintenance 71% of investment is used for such works (Department of the Environment 2008) and the total Australian property stock was worth over $6 trillion in June 2008. Given that building services in commercial buildings typically lasts between 20-30 years and the average age of the stock is 31 years – it appears that many properties are due for adaptation and there is major opportunity for adaptation that alleviates the impact of global warming and climate change.

Uncertainty surfaces such as; how much adaptation of existing stock is typically undertaken? And is the target of 1200 adaptations before 2020 achievable? Furthermore how could the City identify which buildings are most probable to be adapted prior to 2020? This paper details the configuration of a database of Melbourne buildings populated with data about physical, social, economic legislative and environmental attributes. There is a discussion about how the database will be used to determine; how much adaptive reuse has been undertaken historically; if any triggers to adaptation can be identified; and whether any relationships between adaptation physical, social, economic, legislative and environmental attributes and adaptation exist. The relevance of this research is obvious to all policy makers where adaptation of existing commercial buildings is perceived a as key component of delivering sustainability.

A new adaptive blind channel identification algorithm

This paper addresses the blind identification of single-input multiple-output (SIMO) finite-impulse-response (FIR) systems. We first propose a new adaptive algorithm for the blind identification of SIMO FIR systems. Then, its convergence property is analyzed systematically. It is shown that under some mild conditions, the proposed algorithm is guaranteed to converge in the mean to the true channel impulse responses in both noisy and noiseless cases. Simulations are carried out to demonstrate the theoretical results.

Some aspects of adaptive cluster sampling

The adaptive cluster sampling (ACS) procedure is difficult to apply if some of the networks appearing in the sample are large. To deal with such large networks, a two-stage adaptive cluster sampling (TACS) procedure and an adjusted two-stage adaptive cluster sampling (ATACS) procedure are discussed.

Uplink power control via adaptive hmm estimation

Transmit power control is an important consideration in the cellular system design since it increases system capacity, improves QoS and reduces multi-user interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss of the fading channel is presented. Making power control decisions based on the measured received power allows to model the fading channel pathloss dynamics with a Hidden Markov Model. Applying the online HMM identification algorithm enables accurate estimation of the real pathloss which ensures efficient performance of the suggested power control scheme.

Incremental and adaptive clustering stream data over sliding window

Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.