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排序方式: 共有10000条查询结果,搜索用时 15 毫秒
991.
992.
Tae Hyon Kim Kiyohiro Goto Hiroki Igarashi Kazuyuki Kon Noritaka Sato Fumitoshi Matsuno 《Artificial Life and Robotics》2012,16(4):514-518
Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the
real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real
environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in
real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety
of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can
consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated
in the Real World Robot Challenge 2010. The experimental results are given. 相似文献
993.
Ekrem Savaş 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(4):591-595
Quite recently, Sava? (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence \(X=\{X_{k,l}\}\) of fuzzy numbers as follows: a double sequence \(X=\{X_{k,l}\}\) is said to be lacunary P-statistically convergent to \(X_{0}\) provided that for each \(\epsilon >0\) In this paper we introduce and study double lacunary \(\sigma\)-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.
相似文献
$ P-\lim_{r,s}\frac{1}{h_{r,s}}\left | \{(k,l)\in I_{r,s}: d(X_{k,l },X_0)\geq \epsilon\}\right|= 0. $
994.
In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an
expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed.
Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of
features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers
are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for
random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly.
The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries. 相似文献
995.
A K-nearest neighbours method based on imprecise probabilities 总被引:1,自引:1,他引:0
Sebastien Destercke 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):833-844
K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good
performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest
neighbours approach that uses the theory of imprecise probabilities, and more specifically lower previsions. We show that
the proposed approach has several assets: it can handle uncertain data in a very generic way, and decision rules developed
within this theory allow us to deal with conflicting information between neighbours or with the absence of close neighbour
to the instance to classify. We show that results of the basic k-NN and weighted k-NN methods can be retrieved by the proposed approach. We end with some experiments on the classical data sets. 相似文献
996.
Julián Luengo José A. Sáez Francisco Herrera 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):863-881
Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain
good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life
data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several
schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well
known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches
to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which
we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for
FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined
missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation
methods conditioned to the type of FRBCSs is required. 相似文献
997.
Yiqiao Cai Jiahai Wang Jian Yin 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(2):303-330
Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific
and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing
more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper
proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically.
Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS).
In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy.
They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental
studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based
DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least
comparable to several state-of-art DE variants as well as some other evolutionary algorithms. 相似文献
998.
A. M. Palacios M. J. Gacto J. Alcalá-Fdez 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):883-901
Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily
analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining
association rules from databases with crisp values. However, the data in many real-world applications have a certain degree
of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting
knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori
mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early
childhood were made to verify the performance of the proposed algorithm. 相似文献
999.
Ramon Jansana Umberto Rivieccio 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(3):493-504
We introduce a new product bilattice construction that generalizes the well-known one for interlaced bilattices and others
that were developed more recently, allowing to obtain a bilattice with two residuated pairs as a certain kind of power of
an arbitrary residuated lattice. We prove that the class of bilattices thus obtained is a variety, give a finite axiomatization
for it and characterize the congruences of its members in terms of those of their lattice factors. Finally, we show how to
employ our product construction to define first-order definable classes of bilattices corresponding to any first-order definable
subclass of residuated lattices. 相似文献
1000.
Tomá? Kroupa 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(11):1851-1861
We generalise belief functions to many-valued events which are represented by elements of Lindenbaum algebra of infinite-valued ?ukasiewicz propositional logic. Our approach is based on mass assignments used in the Dempster–Shafer theory of evidence. A generalised belief function is totally monotone and it has Choquet integral representation with respect to a unique belief measure on Boolean events. 相似文献