Search

In d - Scattered Set we are given an (edge-weighted) graph and are asked to select at least k vertices, so that the distance between any pair is at least d , thus generalizing Independent Set . We provide upper and lower bounds on the complexity of this problem with respect to various standard graph parameters. In particular, we show the following: • For any d ≥ 2 , an O ∗ ( d tw ) -time algorithm, where tw is the treewidth of the input graph and a tight SETH-based lower bound matching this algorithm’s performance. These generalize known results for Independent Set . • d - Scattered Set is W[1]-hard parameterized by vertex cover (for edge-weighted graphs), or feedback vertex set (for unweighted graphs), even if k is an additional parameter. • A single-exponential algorithm parameterized by vertex cover for unweighted graphs, complementing the above-mentioned hardness. • A 2 O ( td 2 ) -time algorithm parameterized by tree-depth ( td ), as well as a matching ETH-based lower bound, both for unweighted graphs. We complement these mostly negative results by providing an FPT approximation scheme parameterized by treewidth. In particular, we give an algorithm which, for any error parameter ϵ > 0 , runs in time O ∗ ( ( tw ∕ ϵ ) O ( tw ) ) and returns a d ∕ ( 1 + ϵ ) -scattered set of size k , if a d -scattered set of the same size exists.

The critical power (CP) and maximal lactate steady state (MLSS) are operational surrogates of the maximal metabolic steady state (MMSS). However, their concordance and their agreement with MMSS remains variable likely due to methodological factors. PURPOSE To compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS. METHODS After a ramp-test, ten recreationally active males performed four-to-five severe-intensity constant-power output (PO) trials to estimate CP, and three-to-four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the 3-parameter hyperbolic (CP3-hyp), 2-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with lowest combined parameter error identified the "best-fit" CP (CPbest-fit). MLSS was determined as an increase in blood lactate concentration ≤ 1 mM during constant-PO cycling from the 5th (MLSS10-30), 10th (MLSS10-30), 15th (MLSS15-30), 20th (MLSS20-30), or 25th (MLSS25-30) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady state V[Combining Dot Above]O2 (MV[Combining Dot Above]O2ss). RESULTS Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS15-30, MLSS20-30, and MLSS25-30. The PO at MV[Combining Dot Above]O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP2-hyp (244 ± 46 W) and CPlin (248 ± 46 W) and MLSS15-30 and MLSS20-30 (both 245 ± 46 W), respectively displayed, on average, the greatest agreement with MV[Combining Dot Above]O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV[Combining Dot Above]O2ss. CONCLUSIONS Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model-estimate should be considered. For MLSS, MLSS15-30 and MLSS20-30 demonstrated the highest degree of accuracy.

The aim of the study is research of the influence of the sampling interval on the precision of the parameter estimations of a dual-mass DC electromechanical system (DMDCEMS) via Symlet wavelet. Four options are discussed in which the same wavelet function and time of observation are used and only the sampling period is different. The system parameters are evaluated. The relative output error and the summary relative parameter error are also obtained. The results are compared and analyzed.

For GSTAR models, the least squares estimation method is commonly used since errors are assumed be uncorrelated. However, this method is not appropriate when errors are correlated, either in time or spatially. For these cases, the generalized least squares (GLS) method can be applied. GLS is more powerful since it has an error parameter that can act as a controller of the model to produce an efficient estimator. In this study, R Software was used to estimate GSTAR parameters. The resulted model was applied to real data, i.e. the monthly tea production of five plantations in West Java, Indonesia. The best model for forecasting was the GSTAR(1;1) model with temporally correlated error assumption.

This paper presents an optimized predictive current control (PCC) for a Halbach array-linear linear vernier permanent magnet machine(H-LVPMM). The stability and robustness of the PCC are affected by parameter variation. Toward this, a modified proportional integral disturbance observer (PIDO) is proposed to estimate the disturbance voltage value caused by parameter error and inject the correspondent observed component into the control system. Experimental results show the validity of the introduced control scheme.

For critical engine parts, such as crankshafts, fatigue strength is one of the most important parameters in the design and manufacturing stage. In previous work, fatigue limit loads of the crankshafts with different structural features were predicted based on different theories of critical distance (TCDs). The results showed that the definition of the TCD had an obvious impact on the accuracy of such predictions. This paper, first the critical distance was determined based on the limit stress distribution of a given crankshaft and the definition of the approach. Then, the fatigue limit load of another crankshaft was predicted based on the parameters obtained in the previous step. Finally, corresponding experimental verification and model parameter error influence analysis were conducted to evaluate the accuracies of the predictions. The results showed that for the modified indirect-defined TCD (ITCD), the predictions based on the line and point methods were approximately equal, and the parameter errors had an obvious impact on the predictions. However, for the direct-defined TCD (DTCD), the critical line approach had much better accuracy than the critical point approach, and the DTCD was much less sensitive to the model parameter errors than the ITCD approach, therefore, the DTCD approach is much more suitable for actual engineering applications.

The Lagrange multiplier-based method is an effective network parameter error identification method. However, two full matrices with high-dimensions are involved in the calculation procedure; these create huge computational burdens for large-scale power systems. To solve this problem, a fast solution is proposed in this paper, where special treatment techniques for full matrices are used to dramatically improve the calculation efficiency. A practical parameter error identification program has been developed and used in many electric power control centers. In this paper, the results for test systems and on-site applications are given, which show that the proposed approach is very efficient.

In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.

A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.

It was previously shown that normalized Lagrange multipliers in state estimation could be effectively used for network parameter error detection and identification. However, it was also observed that there could exist a number of “insensitive” normalized Lagrange multipliers corresponding to certain parameters which might lead to detection failure. This paper derives the relationship between Lagrange multipliers and parameter errors and explains the reasons behind detection failures. Furthermore, it facilitates the identification of insensitive normalized Lagrange multipliers. These results are then used to clarify the significance of incorporating multiple measurement scans as a way of avoiding detection failure and to derive a new approach for estimating the necessary number of scans to guarantee parameter error detection. Simulation results using the IEEE 14-bus system are presented to verify the proposed solution.

Conditions for boundedness and convergence of the output error and the parameter error for various Caputo's fractional order adaptive schemes based on the steepest descent method are derived in this paper. To this aim, the concept of sufficiently exciting signals is introduced, characterized and related to the concept of persistently exciting signals used in the integer order case. An application is designed in adaptive indirect control of integer order systems using fractional equations to adjust parameters. This application is illustrated for a pole placement adaptive problem. Advantages of using fractional adjustment in control adaptive schemes are experimentally obtained.

Property testing is concerned with constant-time algorithms for deciding whether a given object satisfies a predetermined property or is far from satisfying it. In this paper, we consider testing properties related to the connectivity of two vertices in sparse graphs. We present one-sided error testers for (s,t)-disconnectivity with query complexity 2^O^(^1^/^@e^) for digraphs and O(1/@e^2) for graphs, where @e is an error parameter. Furthermore, we show that these algorithms are the best possible in view of query complexity, i.e., we give matching lower bounds for two-sided error testers for both cases. We also give a constant-time algorithm for testing the (s,t)-disconnectivity of a directed bounded-degree hypergraph, which can be used to test the satisfiability of Horn SAT.

Within the framework of the Zebiak–Cane model, the approach of conditional nonlinear optimal perturbation (CNOP) is used to study the effect of model parameter errors on El Niño–Southern Oscillation (ENSO) predictability. The optimal model parameter errors are obtained within a reasonable error bound (i.e., CNOP-P errors), which have the largest effect on the results of El Niño predictions. The resultant prediction errors were investigated in depth. The CNOP-P errors do not cause a noticeable prediction error of the sea surface temperature anomaly averaged over the Niño-3 region and do not show an obvious season-dependent evolution of the prediction errors. Consequently, the CNOP-P errors do not cause a significant spring predictability barrier (SPB) for El Niño events. In contrast, the initial errors that have the largest effect on the results of the predictions (i.e., the CNOP-I errors) show a season-dependent evolution, with the largest error increase in spring, and also cause a large prediction error, thereby generating a significant SPB. The initial errors play a more important role than the parameter errors in generating a significant SPB for El Niño events. To further validate this result, the authors investigated the situation in which CNOP-I and CNOP-P errors are simultaneously superimposed in the model, which may be a more credible approach because the initial errors and model parameter errors coexist under realistic predictions. The combined mode of CNOP-I and CNOP-P errors shows a similar season-dependent evolution to that of CNOP-I errors and yields a large prediction error, thereby inducing a significant SPB. The inference, therefore, is that initial errors play a more important role than model parameter errors in generating a significant SPB for El Niño predictions of the Zebiak–Cane model. This result helps to clarify the roles of the initial error and parameter error in the development of an SPB, and highlights the role of initial errors, which demonstrates that the SPB could be markedly reduced by improving the initial conditions. The results provide a theoretical basis for improving data assimilation in ENSO predictions.

Given a text T=T[1 ₀ n] and a circular pattern P=P[1 ₀ m], the circular pattern matching (CPM) problem is to find all occurrences of P in T. We present the first algorithm that exploits suffix links to solve the exact CPM (ECPM) problem in O(n log |Σ|) time and O(n) space, where Σ is the symbol alphabet. Then, we present a q-gram-based algorithm for the approximate CPM (ACPM) problem using the idea of bidirectional edit distance. Our algorithm finds all k-approximate occurrences of P in T. We then extend each algorithm to solve the all-against-all variant of the CPM problem for both exact and k-approximate matches. Although the CPM problem has been studied since the 1980s, this is the first attempt on the all-against-all variant, without using a trivial application of standard CPM algorithms. Given a database S=S1$1S2$2 ₀ SZ$Z of Z sequences, our algorithms solve the all-against-all ACPM problem in O(kmaN) time on average and O(kmmN2) worst case, where k is the error parameter, N=∑i=1Z(|Si|+1), ma=N/Z and mm=maxi=1,2,₀ Z{|Si|}. Space complexity is O(N). These can be compared with the O(N2malog ma) average and O(N3log mm) worst case time required by the best available ACPM algorithm.

Some analytical results concerning PI controller tuning based on integrator plus time delay models are worked out and presented. A method for obtaining PI controller parameters, Kp=alpha/(k*tau), and, Ti=beta*tau, which ensures a given prescribed maximum time delay error, dtau_max, to time delay, tau, ratio parameter delta=dau_max/tau, is presented. The corner stone in this method, is a method product parameter, c=alpha*beta. Analytical relations between the PI controller parameters, Ti, and, Kp, and the time delay error parameter, delta, is presented, and we propose the setting, beta=c/a*(delta+1), and, alpha=a/(delta+1), which gives, Ti=c/a*(delta+1)*tau, and Kp=a/((delta+1)*k*tau), where the parameter, a, is constant in the method product parameter, c=alpha*beta. It also turns out that the integral time, Ti, is linear in, delta, and the proportional gain, Kp, inversely proportional to, delta+1. For the original Ziegler Nichols (ZN) method this parameter is approximately, c=2.38, and the presented method may e.g., be used to obtain new modified ZN parameters with increased robustness margins, also documented in the paper.

We present the regression models for determining the total excess air ratio αΣ and gas temperature Tg* upstream of the high-pressure turbine formed on the basis of the engine parameter values that are regularly measured. The sensitivity of the regression models presented to the sensor and instrumentation precision is studied. The errors of measuring and calculating αΣ and Tg* in different altitude-speed engine operation conditions are shown.

This paper deals with an influence of parameter error to be included in a coordinate transformation which transforms a nonholonomic driftless system into a canonical form called “Chained Form”. The considered driftless systems are classified according to this influence, and investigated the possibility whether the system is able to regulate by already proposed control method. Finally adaptive control method within the framework of Time State Control Form for one of the classified system with an unknown system parameter which is difficult to regulate is proposed.

The paper proposes a robust three-view matching method to produce quasi-dense 3D points in multi-view stereo. In order to overcome the limitations of traditional wide baseline match propagation methods, the proposed method simultaneously models illumination variations and perspective distortions in images, and performs an effective affine parameter error detection and rectification mechanism in the process of match propagation. Experiments show the proposed method can effectively enhance the stability and robustness of match propagation, and achieve better performance in both accuracy and quantity of the quasi-dense 3D points.

Teste de propriedades em grafos consiste no estudo de algoritmos aleatórios sublineares que determinam se um grafo $G$ de entrada com $n$ vértices satisfaz uma dada propriedade ou se é necessário adicionar ou remover mais do que $\\epsilon{n \\choose 2}$ arestas para fazer $G$ satisfazê-la, para algum parâmetro $\\epsilon$ de erro fixo. Uma propriedade de grafos $P$ é dita testável se, para todo $\\epsilon > 0$, existe um tal algoritmo para $P$ cujo tempo de execução é independente de $n$. Um dos resultados de maior importância nesta área, provado por Alon e Shapira, afirma que toda propriedade hereditária de grafos é testável. Neste trabalho, apresentamos resultados análogos para torneios --- grafos completos nos quais são dadas orientações para cada aresta. Graph property testing is the study of randomized sublinear algorithms which decide if an input graph $G$ with $n$ vertices satisfies a given property or if it is necessary to add or remove more than $\\epsilon{n \\choose 2}$ edges to make $G$ satisfy it, for some fixed error parameter $\\epsilon$ . A graph property $P$ is called testable if, for every $\\epsilon > 0$, there is such an algorithm for $P$ whose run time is independent of $n$. One of the most important results in this area is due to Alon and Shapira, who showed that every hereditary graph property is testable. In this work, we show analogous results for tournaments --- complete graphs in which every edge is given an orientation.

We consider the related notions of two-prover and of relativistic commitment schemes. In recent work, Lunghi et al. proposed a new relativistic commitment scheme with a multi-round sustain phase that enables to keep the binding property alive as long as the sustain phase is running. They prove security of their scheme against classical attacks; however, the proven bound on the error parameter is very weak: it blows up doubly exponentially in the number of rounds. In this work, we give a new analysis of the multi-round scheme of Lunghi et al., and we show a linear growth of the error parameter instead (also considering classical attacks only). Our analysis is based on a new and rather general composition theorem for two-prover commitment schemes. The proof of our composition theorem is based on a better understanding of the binding property of two-prover commitments that we provide in the form of new definitions and relations among them. These new insights are certainly of independent interest and are likely to be useful in other contexts as well. Finally, our work gives rise to several interesting open problems, for instance extending our results to the quantum setting, where the dishonest provers are allowed to perform measurements on an entangled quantum state in order to try to break the binding property., Comment: Independently and concurrently, Chakraborty, Chailloux, and Leverrier proved a similar bound on the Lunghi et al. scheme (https://arxiv.org/abs/1507.00239) with respect to a weaker notion of security. The latest revision also contains a tightness result similar to the one by Bricout and Chailloux (https://arxiv.org/abs/1608.03820), but with a different proof and a slightly better constant term

One of the longest standing open questions in adaptive control concerns the correctness of the stability claim of the un–normalized model reference scheme proposed by R. V. Monopoli in 1974. Although provably correct solutions to the problem now abound, in particular, it is well–know that adding a normalization to Monopoli’s original scheme ensures global convergence, it is interesting to know whether this technique– driven modification is really necessary or only required to complete the stability proof in the absence of more elaborate arguments. In this paper we construct a counterexample that provides a definite—unfortunately, negative—answer to the claim. Instrumental for the establishment of this result is a technical lemma that shows that, under some conditions on the regressor that may appear in Monopoli’s scheme, the parameter error freezes as the adaptation gain goes to infinity. On the lighter side, we also prove that the counterexample can be “fixed”, in the sense of achieving semiglobal stability, adjusting some tuning parameters.

We present two new algorithms for solving the {\em All Pairs Shortest Paths} (APSP) problem for weighted directed graphs. Both algorithms use fast matrix multiplication algorithms. The first algorithm solves the APSP problem for weighted directed graphs in which the edge weights are integers of small absolute value in $\Ot(n^{2+\mu})$ time, where $\mu$ satisfies the equation $\omega(1,\mu,1)=1+2\mu$ and $\omega(1,\mu,1)$ is the exponent of the multiplication of an $n\times n^\mu$ matrix by an $n^\mu \times n$ matrix. Currently, the best available bounds on $\omega(1,\mu,1)$, obtained by Coppersmith, imply that $\mu0$ is an error parameter and W is the largest edge weight in the graph, after the edge weights are scaled so that the smallest non-zero edge weight in the graph is 1. It returns estimates of all the distances in the graph with a stretch of at most $1+\eps$. Corresponding paths can also be found efficiently., Comment: 27 pages, 19 figures, a preliminary version appeared in FOCS'98 under a slightly different title

×n matrix A with entries between say −1 and 1, and an error parameter e between 0 and 1, we find a matrix D (implicitly) which is the sum of \(\) simple rank 1 matrices so that the sum of entries of any submatrix (among the \(\)) of (A−D) is at most emn in absolute value. Our algorithm takes time dependent only on e and the allowed probability of failure (not on m, n).

The well-known recursive least-squares (RLS) algorithm experiences overshoot in the norm of the parameter error vector temporarily during the initialisation stage when the initial value of the input autocorrelation matrix is chosen very small. We have had no guide to choose a suitable initial value of the input autocorrelation matrix in order to cause the RLS algorithm not to fluctuate during the initialisation phase. In this paper, deriving the bound of parameter error norm square, we give a useful guide to choose the initial value in order to avoid the initial overshoot in the RLS algorithm. Besides, we show that what are the crucial factors of the overshoot phenomena. The ratio of the initial value to the input power should be chosen according to the signal-to-noise ratio of the system and the order of the filter. We present various simulation results to validate our analysis.

In practical computation of the discrete best uniform approximation, we usually only get “near best” (i.e., with the “e-near alteration property”) approximation. We need to estimate the error between the (unknown) best approximation and the achieved approximation. In this paper we estimate the parameter error by means of the generalized strong unicity constants.

The variable precision covering rough set model is promoted by extending the range of error parameter in this paper. Some properties of the promoted variable precision covering rough set model are discussed. Furthermore, we get some meaningful conclusion by means of researching on the power effect of covering variable precision approximation operators for the promoted variable precision covering rough set model.

Given a stable L\'{e}vy process $X=(X_t)_{0\le t\le T}$ of index $\alpha\in(1,2)$ with no negative jumps, and letting $S_t=\sup_{0\le s\le t}X_s$ denote its running supremum for $t\in [0,T]$, we consider the optimal prediction problem \[V=\inf_{0\le\tau\le T}\mathsf{E}(S_T-X_{\tau})^p,\] where the infimum is taken over all stopping times $\tau$ of $X$, and the error parameter $p\in(1,\alpha)$ is given and fixed. Reducing the optimal prediction problem to a fractional free-boundary problem of Riemann--Liouville type, and finding an explicit solution to the latter, we show that there exists $\alpha_*\in(1,2)$ (equal to 1.57 approximately) and a strictly increasing function $p_*:(\alpha_*,2)\rightarrow(1,2)$ satisfying $p_*(\alpha_*+)=1$, $p_*(2-)=2$ and $p_*(\alpha), Comment: Published in at http://dx.doi.org/10.1214/10-AOP598 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Any error of network parameters affects the value of the measurement residuals calculated in state estimation. Explicit mathematical expressions relating the residuals to the parameter errors are derived. A two-step approach is proposed for parameter error estimation. The first step is to estimate a bias vector which combines the effects of parameter errors and the state of the system. A least-square approach using the measurement residuals calculated in each state estimation run is proposed for the first step. After several state estimation runs, a sequence of such bias vectors is obtained. The second step is to estimate the parameter errors from the sequence of bias vectors. A recursive least-square estimation method is proposed for this step. Theoretical and computational issues of the proposed method are addressed. Test results are presented. >

Inverse problems in geophysics are usually ill-posed, in such a way that regularization is one possibility to have more stable solutions. The quality of the regularized solution however depends on the appropriate selection of the regularization parameter λ . One now popular tool for the selection of λ is the so-called L-curve, reintroduced in the literature of inverse problems by Hansen in the 1990’s. L-curve may sometimes provide a non-optimal λ for imaging, so that in the present work we compare three methodologies for the selection of λ . The first one is the λ provided by the Hansen’s toolbox. The second is the visual inspection of the L-curve built by us, and the third one is the critical point of the L-curve first derivative, which is, in general, equivalent to the inflection point, or the L-curve’s knee. This point is the appropriate compensation between the x axis which is the data parameter error, and the y axis, which represents the amount of regularization. Extensive simulations with synthetic data in traveltime tomography leaded us to the conclusion that all three methodologies are feasible, although we noticed that for the zero order regularization, with noisy data, visual inspection and first derivative methodologies provided the best results.

The following sections are included:IntroductionWays of Fitting the Data to the Set of TheoriesError SpecificationsDescribing True PreferencesThe ExperimentsAnalyzing the ResultsCorrecting for Degrees of FreedomFull and Overfull CorrectionCP Errors in the Complete Ranking ExperimentPreference Functionals FittedResultsConclusionsAcknowledgementsReferences

One of the longest standing open questions in adaptive control concerns the correctness of the stability claim of the un-normalized model reference scheme proposed by Monopoli in 1974. Although provably correct solutions to the problem now abound, in particular, it is well known that adding a normalization to Monopoli's original scheme ensures global convergence, it is interesting to know whether this technique-driven modification is really necessary or only required to complete the stability proof in the absence of more elaborate arguments. In this note, we construct a counterexample that provides a definite-unfortunately, negative-answer to the claim. Instrumental for the establishment of this result is a technical lemma that shows that, under some conditions on the regressor that may appear in Monopoli's scheme, the parameter error freezes as the adaptation gain goes to infinity. On the lighter side, we also prove that Monopoli's scheme is semiglobally stable, underscoring the relevance of this important contribution.

The adaptive control of feedback linearizable discrete-time systems is studied. The nonlinearities in the system are linear combinations of known nonlinear functions. Although the unknown parameters appear linearly, the convergence result obtained is not global. The maximum allowable parameter error depends on the system characteristics and on the initial system states. Simulations show that the system diverges when the initial parameter error is too large.

We show that s-term DNF formulas can be learned under the uniform distribution in quasi-polynomial time with statistical queries of tolerance Ω(e/s). The tolerance improves on the known tolerance Ω(e2/s) and is optimal with respect to its dependence on the error parameter e. We further consider the related model of learning with proper distance queries and show that DNF formulas can be learned under the uniform distribution with quasi-polynomial queries, where the hypotheses are DNF formulas of polynomial size. Finally we consider the class of majorities over DNF formulas and provide polynomially related upper and lower bounds for the number of distance queries required to learn this class.

The CSG’s physical performance is decided by its structural parameters, such as width of grating stair, height of grating stair, and the shape of stair edge. Consideration of the practical fabrication process, an error model of CSG’s structure is built to express the relationship between CSG’s performance and its structural parameters. In this model, each structural parameter of CSG is expressed as a variable, and the structural parameter error induced by fabrication process is expressed as minute disturb of relative variable. Through analysis and simulation, we obtained a general estimation standard for the structural parameter errors of CSG, which provides a theoretical direction for CSG’s fabrication and usage.

The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of classifiers mapping a given reward landscape [15,16,29]. XCS is particularly powerful within direct-reward environments, and notably within problems suitable for commercial application [3]. The application of XCS within delayed reward environments has also shown promise, although early investigations were within enviroments with a comparatively short delay to reward (e.g. [28, 19]). Subsequent systematic investigation [19,20,1,2] have suggested that XCS has difficulty accurately mapping and exploiting even simple environments with moderate reward delays. This paper summarises these results and presents new results that identify some limits and their implications. A modification to the error computation within XCS is introduced that allows the minimum error parameter to be applied relative to the magnitude of the payoff to each classifier. First results demonstrate that this modification enables XCS to successfully map longer delayed-reward enviroments.

Following publication of the paper, the authors have detected a parameter error in the Eq. (5) which was used for estimating greenhouse eference evapotranspiration (ET0). The equation of FAO 56 Penman–Monteith method with a fixed aerodynamic resistance of 295 s m−1 hould be: ET0 = 0.408 (Rn − G) + 628 T+273 (es − ea) (5) + 1.24 The ET0 value used in the paper was correct. There were some errors in publication process. No further changes were required in the Results” and “Discussion and Conclusions” sections of the paper. The authors would like to apologise for any inconvenience caused.

A short-coding error parameter (SCEP) that provides a bound on the average probability of decoding error for codes with length shorter than the memory length is presented. It is shown that this bound is tighter than the bound based on the conventional cutoff rate and that the SCEP is independent of the channel's memory length, provided the codeword length is shorter than the memory length. >

The extent to which errors in predicting body segment parameters (SP) influence biomechanical analysis of human motion is unclear. Therefore, the current study quantitatively evaluated the differences in SP estimates using literature predictive functions and computed the effect of SP variation on the kinetic output of walking. For a group of 15 young males, significant differences (P0. 05) were observed between SP estimates for the leg and thigh using the literature predictive functions, with mass and moment of inertial values differing by more than 40%. Using kinematic and ground reaction force data collected for each subject while walking, inverse dynamic analysis was performed iteratively to compute hip forces and moments while simultaneously varying SP values over nine intervals within +/-40% of a baseline value. SP variations were found to significantly affect (P0.05) most of the kinetic estimates produced, particularly those taken during the swing phase. However, the magnitude of these effects was generally less than 1% of body weight. The data from the current study allows researchers to estimate the errors in kinetic measures due to SP variation. The results also indicate that the accuracy of SP prediction should be of concern in biomechanical research particularly for open chain and high acceleration activities. Further study is necessary to identify the importance of SP variation on other motion skills.

Background The role of pharmacokinetic modelling is important in the development of new formulations. Some of these models are related to a particular dosage form, others are similar to models that have already been developed. Intranasal (IN) administration can be an example of a dosage form with a specific pharmacokinetic model, especially when it is applied to create a systemic effect. Purpose To design a pharmacokinetic model that adequately describes a dual absorption profile of the concentration-time curve for intranasal administration. Materials and Methods A strategy to predict dual absorption was developed to describe the pharmacokinetics of an intranasal administration (model1 and model2). A programme for fitting and simulation was developed (SIMLAB). Midazolam nasal spray was used as an example for this model. To validate the final pharmacokinetic model, Monte Carlo simulations were performed. Results We had trouble fitting the observations to a single one-compartment dual absorption model. In many cases a flip-flop condition occurred in which the fitted absorption rate was lower than the estimated elimination rate, and the elimination rate showed an unrealistic value. To prevent this flip-flop condition, we used the absorption parameters from the associated observations. We developed the following model: the model superposes two one-compartment absorption models where the dose is split up over the two compartment inputs and the concentration-time curves are separated by using different lag-times (t0). Monte Carlo simulations resulted in a plasma concentration-time profile, indicating the median concentration and the 5th–95th percentile ranges. Biphasic profiles were observed starting at a parameter error of 15%, increasing to 13.6% of biphasic profiles at a parameter error of 50%. When increasing the difference between a parameter in Model 1 and Model 2, the contribution of t0 to creating a local minimum exceeded the contribution of ka. The AUC of the measured and estimated curve was 201.6 µg/L*h and 201.3 µg/L*h, respectively. Conclusions The model developed is able to fit concentration-time curves showing individual dual absorption curves adequately. No conflict of interest.

We present a new approach to adaptive nonlinear control based on a complete controller-identifier separation which has long been a goal in adaptive system design. Our controllers guarantee certain input/state stability properties with respect to the parameter error % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC % vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbd9wDYLwzYbItLDharyavP1wz % ZbItLDhis9wBH5garqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb % L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe % pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaam % aaeaqbaaGcbiqaca0fcuaH4oqCgaacaaaa!3CEC! \[ \tilde \theta \] and its derivative % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC % vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbd9wDYLwzYbItLDharyavP1wz % ZbItLDhis9wBH5garqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb % L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe % pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaam % aaeaqbaaGcbiqaca0fcuaH4oqCgaacgaGaaaaa!3CF4! \[ \dot \tilde \theta \] as inputs. The parameter identifiers, in turn, guarantee % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC % vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbd9wDYLwzYbItLDharyavP1wz % ZbItLDhis9wBH5garqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb % L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe % pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaam % aaeaqbaaGcbiGacWefca0fcuaH4oqCgaacaiabgIGioprr1ngBPrwt % HrhAXaqehuuDJXwAKbstHrhAG8KBLbacgaGae8NeHWKaeyOhIukaaa!4AD4! \[ \tilde \theta \in \mathcal{L}\infty \] , and either % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC % vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbd9wDYLwzYbItLDharyavP1wz % ZbItLDhis9wBH5garqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb % L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe % pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaam % aaeaqbaaGcbiGacWefca0fcuaH4oqCgaacgaGaaiabgIGioprr1ngB % PrwtHrhAXaqehuuDJXwAKbstHrhAG8KBLbacgaGae8NeHWKaeyOhIu % kaaa!4ADC! \[ \dot \tilde \theta \in \mathcal{L}\infty \] or % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC % vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbd9wDYLwzYbItLDharyavP1wz % ZbItLDhis9wBH5garqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb % L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe % pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaam % aaeaqbaaGcbiGacWefca0fcuaH4oqCgaacgaGaaiabgIGioprr1ngB % PrwtHrhAXaqehuuDJXwAKbstHrhAG8KBLbacgaGae8NeHW0aaSbaaS % qaaiabikdaYaqabaaaaa!4A89! \[ \dot \tilde \theta \in \mathcal{L}_2 \] or both. This estimation-based approach encompases two families of schemes: swapping-based and observer-based. Swapping-based schemes allow the use of a wide variety of update laws — gradient and least-squares, normalized and unnormalized. Observer-based schemes use parameter identifiers of lower dynamic order. All these schemes achieve systematic improvement of transient performance.

An online degradation model is presented, in which is avoided the error in parameter degradation accelerated test, which is caused by temperature shock during parameter measurement. Through the measurement with avoiding the error parameter, the parameter degradation model can be more accurate. To demonstrate the application of the method, a kind of mature product, 3CG120, is tested in a temperature range of 150230 ℃ under online process-stress. Then the error of lifetime is obtained by utilizing the online model to be about 6.5%, much less than that of the old model (23.2%).

It often happens that the solution of a minimum problem is itself straightforward, but the calculation or interpretation of the resulting parameter uncertainties, as determined by the shape of the function at the minimum, is considerably more complicated. The purpose of this note is to clarify the most commonly encountered difficulties in parameter error determination. These difficulties may arise in connection with any fitting program, but will be discussed here with the terminology of the program MINUIT for the convenience of MINUIT users. The most common causes of misinterpretation may be grouped into three categories: 1. 1. Proper normalization of the user-supplied chi-square or likelihood function, and appropriate ERROR DEF. 2. 2. Non-linearities in the problem formulation, leading to different errors being calculated by different techniques, such as 3. MIGRAD, HESSE and MINOS. 4. 3. Multiparameter error definition and interpretation. All these topic are discussed in some detail by Eadie et al., which may be consulted for further details.

The behavior of identifiers and adaptive observers in the presence of parasitics is analyzed. Bounds for the state/parameter error are established. The adaptive schemes considered are shown to be robust provided the input signal is rich for the dominant modes, but does not contain high frequencies in the parasitic range. The bounds indicate possibilities for reducing the error by a proper choice of the input signal. Copyright © 1982 by The Institute of Electrical and Electronics Engineers, Inc. 27 4 921 927

Solutions of a two-variable nonlinear model were studied by randomly selecting parameters from independent triangular distributions, with maximum and minimum parameter values equal to 10% of the expected value. Small coefficients of variation on parameter values can result in coefficients of variation on model output that are as much as an order of magnitude larger. Establishing limits of acceptable system behavior (error criteria at four points in time reduced the total error and revealed significant variance-covariance relationships between parameters. With parameters drawn from a prespecified variance-covariance matrix, total error over the entire simulation was reduced by a factor of 4.8. Our results can be applied to improve the design of experiments used to estimate parameter values and validate model output.

When the SPR condition is not satisfied then there exist input signals for which the considered output error algorithm is locally unstable. A condition is given which delimits the sharp stability-instability boundary in the case of slow estimation, whereas local stability properties are guaranteed by a more conservative signal-dependent average SPR condition. These conditions are also illustrated by an example.

A theorem is derived which specifies a sufficient number of input frequencies to guarantee identification of an unknown noise free linear plant. Since all of the referenced work relates to sufficient conditions for nulling of the parameter error vector, it is to be expected that various conditions on the system to be identified will have been imposed. It was found that some of these conditions appear to be necessary while others do not. The main contribution is to provide a theorem which considers the effect of unknown parameters in the state equation upon the frequency requirements.

This paper examines robustness issues in Model Reference Adaptive Control systems in the presence of unmodeled dynamics and output disturbances. We present an approximate technique, trend analysis, by which we can study the evolution of the parameter error trajectory under periodic excitation. This analysis provides new insights upon the size and spectral content of the excitation sufficient to guarantee local stability.

A complete procedure for generating and analysing robust model reference adaptive control schemes for multivariable plants is developed. The procedure consists of two parts: the first part involves the characterization of the integral structure of the modelled part of the plant, and the associated parametrization of the controller structure and the second part involves the development of a general robust adaptive law for adjusting the controller parameters so that the closed‐loop plant is globally stable despite the presence of unmodelled dynamics and bounded disturbances. The use of dominantly rich signals for improving convergence and the bounds for the tracking and parameter error is also analysed. Copyright © 1988 John Wiley & Sons, Ltd. 2 3 217 248 Cited By :40