125 research outputs found
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
Computational Intelligence Algorithms for Optimisation of Wireless Sensor Networks
Recent studies have tended towards incorporating Computation Intelligence,
which is a large umbrella for all Machine Learning and Metaheuristic
approaches into wireless sensor network (WSN) applications
for enhanced and intuitive performance. Meta-heuristic optimisation
techniques are used for solving several WSN issues such as energy
minimisation, coverage, routing, scheduling and so on. This research
designs and develops highly intelligent WSNs that can provide the
core requirement of energy efficiency and reliability. To meet these
requirements, two major decisions were carried out at the sink node
or base station. The first decision involves the use of supervised and
unsupervised machine learning algorithms to achieve an accurate decision
at the sink node. This thesis presents a new hybrid approach
for event (fire) detection system using k-means clustering on aggregated
fire data to form two class labels (fire and non-fire). The resulting
data outputs are trained and tested by the Feed Forward Neural
Network, Naive Bayes, and Decision Trees classifier. This hybrid approach
was found to significantly improve fire detection performance
against the use of only the classifiers. The second decision employs
a metaheuristic approach to optimise the solution of WSNs clustering
problem. Two metaheuristic-based protocols namely the Dynamic
Local Search Algorithm for Clustering Hierarchy (DLSACH) and Heuristics
Algorithm for Clustering Hierarchy (HACH) are proposed to achieve
an evenly balanced energy and minimise the net residual energy of
each sensor nodes. This thesis proved that the two protocols outperforms
state-of-the-art protocols such as LEACH, TCAC and SEECH
in terms of network lifetime and maintains a favourable performance
even under different energy heterogeneity settings
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