共查询到10条相似文献,搜索用时 140 毫秒
1.
Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability
and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds
of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives
in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership
functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary
algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that
could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work
presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still
accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two
different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search
ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the
same time simpler models with respect to the single objective based approach.
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2.
This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary
Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition
and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational
intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process,
we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.
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3.
Backfitting of fuzzy rules is an Iterative Rule Learning technique for obtaining the knowledge base of a fuzzy rule-based
system in regression problems. It consists in fitting one fuzzy rule to the data, and replacing the whole training set by
the residual of the approximation. The obtained rule is added to the knowledge base, and the process is repeated until the
residual is zero, or near zero. Such a design has been extended to imprecise data for which the observation error is small.
Nevertheless, when this error is moderate or high, the learning can stop early. In this kind of algorithms, the specificity
of the residual might decrease when a new rule is added. There may happen that the residual grows so wide that it covers the
value zero for all points (thus the algorithm stops), but we have not yet extracted all the information available in the dataset.
Focusing on this problem, this paper is about datasets with medium to high discrepancies between the observed and the actual
values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality
of the iterative learning degrades in this kind of problems, because it does not make full use of all the available information.
As an alternative to sequentially obtaining rules, we propose a new multiobjective Genetic Cooperative Competitive Learning
(GCCL) algorithm. In our approach, each individual in the population codifies one rule, which competes in the population in
terms of maximum coverage and fitting, while the individuals in the population cooperate to form the knowledge base.
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4.
We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed
about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked
on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive,
although they had several important reservations about camera-phone technology more generally. Data from our pointing-device
task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device
trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings,
design lessons for camera-phone and visual-tag applications are presented.
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5.
In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence
of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in
families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology
we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls
to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls
in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities
show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness
performances efficiently.
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6.
Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed. 相似文献
7.
Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories.
Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest
that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same
time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and
freedom. The analysis of these concerns requires reflecting on the availability of scientific models accounting for key aspects
of sensorimotor coordination and plastic adaptation mechanisms in the brain.
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8.
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these
algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive
information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one
to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods,
instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving
clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general
features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better
protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated.
It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well
as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to
which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.
In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then,
we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations
about future work and promising directions in the context of privacy preservation in data mining are discussed.
*The work reported in this paper has been partially supported by the EU under the IST Project CODMINE and by the Sponsors of
CERIAS.
Editor: Geoff Webb
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9.
Remotely sensed hyperspectral imagery has many important applications since its high-spectral resolution enables more accurate
object detection and classification. To support immediate decision-making in critical circumstances, real-time onboard implementation
is greatly desired. This paper investigates real-time implementation of several popular detection and classification algorithms
for image data with different formats. An effective approach to speeding up real-time implementation is proposed by using
a small portion of pixels in the evaluation of data statistics. An empirical rule of an appropriate percentage of pixels to
be used is investigated, which results in reduced computational complexity and simplified hardware implementation. An overall
system architecture is also provided.
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10.
Thermal functional circuits are an interesting group of the MEMS elements with high a potential. A practical realisation is
called Quadratic Transfer Characteristics (QTC) element of which driving principle is the Seebeck effect. Such devices can
be applied e.g. as RMS meters. In this paper we are dealing with the analyses of a QTC element from different perspectives.
A family of compact models is presented. These models are suitable to use in network simulation programs. To investigate the
detailed behaviour of the device, we measured a few secondary properties of the structure, such as temperature dependence,
cut-off frequency and non-quadratic error.
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