18,998 research outputs found
Parameter Estimation of Switched Hammerstein Systems
This paper deals with the parameter estimation problem of the
Single-Input-Single-Output (SISO) switched Hammerstein system. Suppose that the
switching law is arbitrary but can be observed online. All subsystems are
parameterized and the Recursive Least Squares (RLS) algorithm is applied to
estimate their parameters. To overcome the difficulty caused by coupling of
data from different subsystems, the concept "intrinsic switch" is introduced.
Two cases are considered: i) The input is taken to be a sequence of independent
identically distributed (i.i.d.) random variables when identification is the
only purpose; ii) A diminishingly excited signal is superimposed on the control
when the adaptive control law is given. The strong consistency of the estimates
in both cases is established and a simulation example is given to verify the
theoretical analysis.Comment: 16 pages, 3 figures; Accepted for publication by Acta Mathematicae
Applicatae Sinica (http://link.springer.com/journal/10255
Modeling, Analysis, and Hard Real-time Scheduling of Adaptive Streaming Applications
In real-time systems, the application's behavior has to be predictable at
compile-time to guarantee timing constraints. However, modern streaming
applications which exhibit adaptive behavior due to mode switching at run-time,
may degrade system predictability due to unknown behavior of the application
during mode transitions. Therefore, proper temporal analysis during mode
transitions is imperative to preserve system predictability. To this end, in
this paper, we initially introduce Mode Aware Data Flow (MADF) which is our new
predictable Model of Computation (MoC) to efficiently capture the behavior of
adaptive streaming applications. Then, as an important part of the operational
semantics of MADF, we propose the Maximum-Overlap Offset (MOO) which is our
novel protocol for mode transitions. The main advantage of this transition
protocol is that, in contrast to self-timed transition protocols, it avoids
timing interference between modes upon mode transitions. As a result, any mode
transition can be analyzed independently from the mode transitions that
occurred in the past. Based on this transition protocol, we propose a hard
real-time analysis as well to guarantee timing constraints by avoiding
processor overloading during mode transitions. Therefore, using this protocol,
we can derive a lower bound and an upper bound on the earliest starting time of
the tasks in the new mode during mode transitions in such a way that hard
real-time constraints are respected.Comment: Accepted for presentation at EMSOFT 2018 and for publication in IEEE
Transactions on Computer-Aided Design of Integrated Circuits and Systems
(TCAD) as part of the ESWEEK-TCAD special issu
Axioms for graph clustering quality functions
We investigate properties that intuitively ought to be satisfied by graph
clustering quality functions, that is, functions that assign a score to a
clustering of a graph. Graph clustering, also known as network community
detection, is often performed by optimizing such a function. Two axioms
tailored for graph clustering quality functions are introduced, and the four
axioms introduced in previous work on distance based clustering are
reformulated and generalized for the graph setting. We show that modularity, a
standard quality function for graph clustering, does not satisfy all of these
six properties. This motivates the derivation of a new family of quality
functions, adaptive scale modularity, which does satisfy the proposed axioms.
Adaptive scale modularity has two parameters, which give greater flexibility in
the kinds of clusterings that can be found. Standard graph clustering quality
functions, such as normalized cut and unnormalized cut, are obtained as special
cases of adaptive scale modularity.
In general, the results of our investigation indicate that the considered
axiomatic framework covers existing `good' quality functions for graph
clustering, and can be used to derive an interesting new family of quality
functions.Comment: 23 pages. Full text and sources available on:
http://www.cs.ru.nl/~T.vanLaarhoven/graph-clustering-axioms-2014
A Bayesian Reflection on Surfaces
The topic of this paper is a novel Bayesian continuous-basis field
representation and inference framework. Within this paper several problems are
solved: The maximally informative inference of continuous-basis fields, that is
where the basis for the field is itself a continuous object and not
representable in a finite manner; the tradeoff between accuracy of
representation in terms of information learned, and memory or storage capacity
in bits; the approximation of probability distributions so that a maximal
amount of information about the object being inferred is preserved; an
information theoretic justification for multigrid methodology. The maximally
informative field inference framework is described in full generality and
denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the
update of field knowledge from previous knowledge at any scale, and new data,
to new knowledge at any other scale. An application example instance, the
inference of continuous surfaces from measurements (for example, camera image
data), is presented.Comment: 34 pages, 1 figure, abbreviated versions presented: Bayesian
Statistics, Valencia, Spain, 1998; Maximum Entropy and Bayesian Methods,
Garching, Germany, 199
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