722 research outputs found
Using optimisation techniques to granulise rough set partitions
Rough set theory (RST) is concerned with the formal approximation of crisp sets
and is a mathematical tool which deals with vagueness and uncertainty. RST can be
integrated into machine learning and can be used to forecast predictions as well as to
determine the causal interpretations for a particular data set. The work performed
in this research is concerned with using various optimisation techniques to granulise
the rough set input partitions in order to achieve the highest forecasting accuracy
produced by the rough set. The forecasting accuracy is measured by using the area
under the curve (AUC) of the receiver operating characteristic (ROC) curve. The
four optimisation techniques used are genetic algorithm, particle swarm optimisation,
hill climbing and simulated annealing. This newly proposed method is tested
on two data sets, namely, the human immunodeficiency virus (HIV) data set and
the militarised interstate dispute (MID) data set. The results obtained from this
granulisation method are compared to two previous static granulisation methods,
namely, equal-width-bin and equal-frequency-bin partitioning. The results conclude
that all of the proposed optimised methods produce higher forecasting accuracies
than that of the two static methods. In the case of the HIV data set, the hill climbing
approach produced the highest accuracy, an accuracy of 69.02% is achieved in a
time of 12624 minutes. For the MID data, the genetic algorithm approach produced
the highest accuracy. The accuracy achieved is 95.82% in a time of 420 minutes.
The rules generated from the rough set are linguistic and easy-to-interpret, but this
does come at the expense of the accuracy lost in the discretisation process where
the granularity of the variables are decreased
A "metric" semi-Lagrangian Vlasov-Poisson solver
We propose a new semi-Lagrangian Vlasov-Poisson solver. It employs elements
of metric to follow locally the flow and its deformation, allowing one to find
quickly and accurately the initial phase-space position of any test
particle , by expanding at second order the geometry of the motion in the
vicinity of the closest element. It is thus possible to reconstruct accurately
the phase-space distribution function at any time and position by
proper interpolation of initial conditions, following Liouville theorem. When
distorsion of the elements of metric becomes too large, it is necessary to
create new initial conditions along with isotropic elements and repeat the
procedure again until next resampling. To speed up the process, interpolation
of the phase-space distribution is performed at second order during the
transport phase, while third order splines are used at the moments of
remapping. We also show how to compute accurately the region of influence of
each element of metric with the proper percolation scheme. The algorithm is
tested here in the framework of one-dimensional gravitational dynamics but is
implemented in such a way that it can be extended easily to four or
six-dimensional phase-space. It can also be trivially generalised to plasmas.Comment: 32 pages, 14 figures, accepted for publication in Journal of Plasma
Physics, Special issue: The Vlasov equation, from space to laboratory plasma
Investigation on soft computing techniques for airport environment evaluation systems
Spatial and temporal information exist widely in engineering fields, especially
in airport environmental management systems. Airport environment is influenced
by many different factors and uncertainty is a significant part of the
system. Decision support considering this kind of spatial and temporal information
and uncertainty is crucial for airport environment related engineering
planning and operation. Geographical information systems and computer aided
design are two powerful tools in supporting spatial and temporal information
systems. However, the present geographical information systems and computer
aided design software are still too general in considering the special features in
airport environment, especially for uncertainty. In this thesis, a series of parameters
and methods for neural network-based knowledge discovery and training
improvement are put forward, such as the relative strength of effect, dynamic
state space search strategy and compound architecture. [Continues.
CHAMP: Creating Heuristics via Many Parameters for online bin packing
The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. We investigate a ‘policy matrix’ representation which assigns a score for each decision option independently and the option with the highest value is chosen for one dimensional online bin packing. A policy matrix might also be considered as a heuristic with many parameters, where each parameter value is a score. We hence investigate a framework which can be used for creating heuristics via many parameters. The proposed framework combines a Genetic Algorithm optimiser, which searches the space of heuristics in policy matrix form, and an online bin packing simulator, which acts as the evaluation function. The empirical results indicate the success of the proposed approach, providing the best solutions for almost all item sequence generators used during the experiments. We also present a novel fitness landscape analysis on the search space of policies. This study hence gives evidence of the potential for automated discovery by intelligent systems of powerful heuristics for online problems; reducing the need for expensive use of human expertise
Model-Based Evaluation of Signal-to-Clutter Ratio for Landmine Detection Using Ground-Penetrating Radar
A regression model is developed in order to estimate in real time the signal-to-clutter ratio (SCR) for landmine detection using ground-penetrating radar. Artificial neural networks are employed in order to express SCR with respect to the soil's properties, the depth of the target, and the central frequency of the pulse. The SCR is synthetically evaluated for a wide range of diverse and controlled scenarios using the finite-difference time-domain method. Fractals are used to describe the geometry of the soil's heterogeneities as well as the roughness of the surface. The dispersive dielectric properties of the soil are expressed with respect to traditionally used soil parameters, namely, sand fraction, clay fraction, water fraction, bulk density, and particle density. Through this approach, a coherent and uniformly distributed training set is created. The overall performance of the resulting nonlinear function is evaluated using scenarios which are not included in the training process. The calculated and the predicted SCR are in good agreement, indicating the validity and the generalization capabilities of the suggested framework
Generalized closed sets in ditopological texture spaces with application in rough set theory
In this paper, the counterparts of generalized open (g-open) and generalized closed (g-closed) sets for ditopological texture spaces are introduced and some of their characterizations are obtained. Some characterizations are presented for generalized bicontinuous difunctions. Also, we introduce new notions of compactness and stability in ditopological texture spaces based on the notion of g-open and g-closed sets. Finally, as an application of g-open and g-closed sets, we generalize the subsystem based denition of rough set theory by using new subsystem, called generalized open sets to dene new types of lower and upper approximation operators, called g-lower and g-upper approximations. These decrease the upper approximation and increase the lower approximation and hence increase the accuracy. Properties of these approximations are studied. An example of multi-valued information systems are given
Random neural networks for rough volatility
We construct a deep learning-based numerical algorithm to solve
path-dependent partial differential equations arising in the context of rough
volatility. Our approach is based on interpreting the PDE as a solution to an
SPDE, building upon recent insights by Bayer, Qiu and Yao, and on constructing
a neural network of reservoir type as originally developed by Gonon,
Grigoryeva, Ortega. The reservoir approach allows us to formulate the
optimisation problem as a simple least-square regression for which we prove
theoretical convergence properties.Comment: 33 pages, 3 figure
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