51,541 research outputs found
Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach
Superposition of sigmoid function over a finite time interval is shown to be
equivalent to the linear combination of the solutions of a linearly
parameterized system of logistic differential equations. Due to the linearity
with respect to the parameters of the system, it is possible to design an
effective procedure for parameter adjustment. Stability properties of this
procedure are analyzed. Strategies shown in earlier studies to facilitate
learning such as randomization of a learning sequence and adding specially
designed disturbances during the learning phase are requirements for
guaranteeing convergence in the learning scheme proposed.Comment: 30 pages, 7 figure
Location-Quality-aware Policy Optimisation for Relay Selection in Mobile Networks
Relaying can improve the coverage and performance of wireless access
networks. In presence of a localisation system at the mobile nodes, the use of
such location estimates for relay node selection can be advantageous as such
information can be collected by access points in linear effort with respect to
number of mobile nodes (while the number of links grows quadratically).
However, the localisation error and the chosen update rate of location
information in conjunction with the mobility model affect the performance of
such location-based relay schemes; these parameters also need to be taken into
account in the design of optimal policies. This paper develops a Markov model
that can capture the joint impact of localisation errors and inaccuracies of
location information due to forwarding delays and mobility; the Markov model is
used to develop algorithms to determine optimal location-based relay policies
that take the aforementioned factors into account. The model is subsequently
used to analyse the impact of deployment parameter choices on the performance
of location-based relaying in WLAN scenarios with free-space propagation
conditions and in an measurement-based indoor office scenario.Comment: Accepted for publication in ACM/Springer Wireless Network
Calculation of an approximate solution of the infinite time-varying LQ-problem
Linear Programming;Algorithm;Optimal Control;operations research
Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG
The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS)
algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K
training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set
Remote State Estimation with Smart Sensors over Markov Fading Channels
We consider a fundamental remote state estimation problem of discrete-time
linear time-invariant (LTI) systems. A smart sensor forwards its local state
estimate to a remote estimator over a time-correlated -state Markov fading
channel, where the packet drop probability is time-varying and depends on the
current fading channel state. We establish a necessary and sufficient condition
for mean-square stability of the remote estimation error covariance as
, where denotes the
spectral radius, is the state transition matrix of the LTI system,
is a diagonal matrix containing the packet drop probabilities in
different channel states, and is the transition probability matrix
of the Markov channel states. To derive this result, we propose a novel
estimation-cycle based approach, and provide new element-wise bounds of matrix
powers. The stability condition is verified by numerical results, and is shown
more effective than existing sufficient conditions in the literature. We
observe that the stability region in terms of the packet drop probabilities in
different channel states can either be convex or concave depending on the
transition probability matrix . Our numerical results suggest that
the stability conditions for remote estimation may coincide for setups with a
smart sensor and with a conventional one (which sends raw measurements to the
remote estimator), though the smart sensor setup achieves a better estimation
performance.Comment: The paper has been accepted by IEEE Transactions on Automatic
Control. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Optimal adaptive filter realizations for sampled stochastic processes with an unknown parameter
Optimal adaptive filter for sampled stochastic processes with unknown paramete
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