36,924 research outputs found
Neural Simulation of Water Systems for Efficient State Estimation
This paper presents a neural network based technique for the
solution of a water system state estimation problem.The technique
combines a neural linear equations solver with a Newton-Raphson
iterations to obtain a solution to an overdetermined set of
nonlinear equations.
The algorithm has been applied to a realistic 34-node water
network. By changing the values of neural network parameters
both the least squares (LS) and least absolute values (LAV)
estimates have been obtained and assessed with respect to their
sensitivity to measurement errors
Optimal experiment design in a filtering context with application to sampled network data
We examine the problem of optimal design in the context of filtering multiple
random walks. Specifically, we define the steady state E-optimal design
criterion and show that the underlying optimization problem leads to a second
order cone program. The developed methodology is applied to tracking network
flow volumes using sampled data, where the design variable corresponds to
controlling the sampling rate. The optimal design is numerically compared to a
myopic and a naive strategy. Finally, we relate our work to the general problem
of steady state optimal design for state space models.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Random Neural Networks and Optimisation
In this thesis we introduce new models and learning algorithms for the Random
Neural Network (RNN), and we develop RNN-based and other approaches for the
solution of emergency management optimisation problems.
With respect to RNN developments, two novel supervised learning algorithms are
proposed. The first, is a gradient descent algorithm for an RNN extension model
that we have introduced, the RNN with synchronised interactions (RNNSI), which
was inspired from the synchronised firing activity observed in brain neural circuits.
The second algorithm is based on modelling the signal-flow equations in RNN as a
nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory
quasi-Newton algorithm specifically designed for the RNN case.
Regarding the investigation of emergency management optimisation problems,
we examine combinatorial assignment problems that require fast, distributed and
close to optimal solution, under information uncertainty. We consider three different
problems with the above characteristics associated with the assignment of
emergency units to incidents with injured civilians (AEUI), the assignment of assets
to tasks under execution uncertainty (ATAU), and the deployment of a robotic
network to establish communication with trapped civilians (DRNCTC).
AEUI is solved by training an RNN tool with instances of the optimisation problem
and then using the trained RNN for decision making; training is achieved using
the developed learning algorithms. For the solution of ATAU problem, we introduce
two different approaches. The first is based on mapping parameters of the
optimisation problem to RNN parameters, and the second on solving a sequence of
minimum cost flow problems on appropriately constructed networks with estimated
arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer
linear programming formulation, which is based on network flows. Finally, we design
and implement distributed heuristic algorithms for the deployment of robots
when the civilian locations are known or uncertain
- …