Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

Singular spectral analysis of ill-known signals and its application to predictive maintenance of windmills with SCADA records

Abstract  A generalization of the singular spectral analysis (SSA) technique to ill-defined data is introduced in this paper. The proposed
algorithm achieves tight estimates of the energy of irregular or aperiodic oscillations from records o…

Abstract  

A generalization of the singular spectral analysis (SSA) technique to ill-defined data is introduced in this paper. The proposed
algorithm achieves tight estimates of the energy of irregular or aperiodic oscillations from records of interval or fuzzy-valued
signals. Fuzzy signals are given a possibilistic interpretation as families of nested confidence intervals. In this context,
some types of Supervisory Control And Data Analysis (SCADA) records, where the minimum, mean and maximum values of the signal
between two scans are logged, are regarded as fuzzy constrains of the values of the sampled signal. The generalized SSA of
these records produces a set of interval-valued or fuzzy coefficients, that bound the spectral transform of the SCADA data.
Furthermore, these bounds are compared to the expected energy of AR(1) red noise, and the irrelevant components are discarded.
This comparison is accomplished using statistical tests for low quality data, that are in turn consistent with the possibilistic
interpretation of a fuzzy signal mentioned before. Generalized SSA has been applied to solve a real world problem, with SCADA
data taken from 40 turbines in a Spanish wind farm. It was found that certain oscillations in the pressure at the hydraulic
circuit of the tip brakes are correlated to long term damages in the windmill gear, showing that this new technique is useful
as a failure indicator in the predictive maintenance of windmills.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-14
  • DOI 10.1007/s00500-011-0767-3
  • Authors
    • Luciano Sánchez, Computer Science Department, University of Oviedo, Campus de Viesques, 33071 Gijón, Asturias, Spain
    • Inés Couso, Facultad de Ciencias, Statistics Department, University of Oviedo, 33071 Oviedo, Asturias, Spain

Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”

Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”
Content Type Journal ArticleCategory EditorialPages 1-2DOI 10.1007/s00500-011-0765-5Authors
Luciano Sánchez, Comput…

Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”

  • Content Type Journal Article
  • Category Editorial
  • Pages 1-2
  • DOI 10.1007/s00500-011-0765-5
  • Authors
    • Luciano Sánchez, Computer Science Department, University of Oviedo, Campus de Viesques, 33071 Gijón, Asturias, Spain
    • Inés Couso, Statistics Department, University of Oviedo, Facultad de Ciencias, 33071 Oviedo, Asturias, Spain

A generalization of the Chebyshev type inequalities for Sugeno integrals

Abstract  In this paper, we give a generalization of the Chebyshev type inequalities for Sugeno integral with respect to non-additive
measures. The main results of this paper generalize most of the inequalities for Sugeno integral obtained b…

Abstract  

In this paper, we give a generalization of the Chebyshev type inequalities for Sugeno integral with respect to non-additive
measures. The main results of this paper generalize most of the inequalities for Sugeno integral obtained by many researchers.
Also, some conclusions are drawn and some problems for further investigations are given.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-8
  • DOI 10.1007/s00500-011-0764-6
  • Authors
    • Hamzeh Agahi, Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
    • Adel Mohammadpour, Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
    • S. Mansour Vaezpour, Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

On normal-valued basic pseudo-hoops

Abstract  We show that every pseudo-hoop satisfies the Riesz decomposition property. We visualize basic pseudo-hoops by functions on
a linearly ordered set. Finally, we study normal-valued basic pseudo-hoops giving a countable base of equati…

Abstract  

We show that every pseudo-hoop satisfies the Riesz decomposition property. We visualize basic pseudo-hoops by functions on
a linearly ordered set. Finally, we study normal-valued basic pseudo-hoops giving a countable base of equations for them.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-10
  • DOI 10.1007/s00500-011-0763-7
  • Authors
    • Michal Botur, Department of Algebra and Geometry, Faculty of Sciences, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic
    • Anatolij Dvurečenskij, Mathematical Institute, Slovak Academy of Sciences, Štefánikova 49, 814 73 Bratislava, Slovakia
    • Tomasz Kowalski, Department of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia

n-Fold implicative basic logic is Gödel logic

Abstract  We prove that Haveshki’s and Eslami’s n-fold implicative basic logic is Gödel logic and n-fold positive implicative basic logic is a fragment of ukasiewicz logic.

Content Type Journal ArticleCategory Original PaperPages 1-…

Abstract  

We prove that Haveshki’s and Eslami’s n-fold implicative basic logic is Gödel logic and n-fold positive implicative basic logic is a fragment of ukasiewicz logic.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-5
  • DOI 10.1007/s00500-011-0761-9
  • Authors
    • Esko Turunen, Tampere University Technology, Tampere, Finland
    • Nganteu Tchikapa, University of Dschang, Dschang, Cameroon
    • Celestin Lele, University of Dschang, Dschang, Cameroon

Rule extraction algorithm from support vector machines and its application to credit screening

Abstract  Developing rule extraction algorithms from machine learning techniques such as artificial neural networks and support vector
machines (SVMs), which are considered incomprehensible black-box models, is an important topic in current …

Abstract  

Developing rule extraction algorithms from machine learning techniques such as artificial neural networks and support vector
machines (SVMs), which are considered incomprehensible black-box models, is an important topic in current research. This study
proposes a rule extraction algorithm from SVMs that uses a kernel-based clustering algorithm to integrate all support vectors
and genetic algorithms into extracted rule sets. This study uses measurements of accuracy, sensitivity, specificity, coverage,
fidelity and comprehensibility to evaluate the performance of the proposed method on the public credit screening data sets.
Results indicate that the proposed method performs better than other rule extraction algorithms. Thus, the proposed algorithm
is an essential analysis tool that can be effectively used in data mining fields.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-14
  • DOI 10.1007/s00500-011-0762-8
  • Authors
    • Chao-Ton Su, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101, Sec. 2, Kuang Fu Road, Hsinchu, 300 Taiwan
    • Yan-Cheng Chen, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101, Sec. 2, Kuang Fu Road, Hsinchu, 300 Taiwan

Impacts of sampling strategies in tournament selection for genetic programming

Abstract  Tournament selection is one of the most commonly used parent selection schemes in genetic programming (GP). While it has a
number of advantages over other selection schemes, it still has some issues that need to be thoroughly inves…

Abstract  

Tournament selection is one of the most commonly used parent selection schemes in genetic programming (GP). While it has a
number of advantages over other selection schemes, it still has some issues that need to be thoroughly investigated. Two of
the issues are associated with the sampling process from the population into the tournament. The first one is the so-called
“multi-sampled” issue, where some individuals in the population are picked up (sampled) many times to form a tournament. The
second one is the “not-sampled” issue, meaning that some individuals are never picked up when forming tournaments. In order
to develop a more effective selection scheme for GP, it is necessary to understand the actual impacts of these issues in standard
tournament selection. This paper investigates the behaviour of different sampling replacement strategies through mathematical
modelling, simulations and empirical experiments. The results show that different sampling replacement strategies have little
impact on selection pressure and cannot effectively tune the selection pressure in dynamic evolution. In order to conduct
effective parent selection in GP, research focuses should be on developing automatic and dynamic selection pressure tuning
methods instead of alternative sampling replacement strategies. Although GP is used in the empirical experiments, the findings
revealed in this paper are expected to be applicable to other evolutionary algorithms.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-19
  • DOI 10.1007/s00500-011-0760-x
  • Authors
    • Huayang Xie, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
    • Mengjie Zhang, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems

Abstract  This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective
traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of…

Abstract  

This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective
traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of scalar optimization subproblems
using Tchebycheff approach. To suit for decomposition, an ant colony is divided into many subcolonies in an overlapped manner,
each of which is for one subproblem. Then each subcolony independently optimizes its corresponding subproblem using single-objective
ant colony optimization algorithm and all subcolonies simultaneously work. During the iteration, each subproblem maintains
an aggregated pheromone trail and an aggregated heuristic matrix. Each subcolony uses the information to solve its corresponding
subproblem. After an iteration, a pheromone trail share procedure is evoked to realize the information share of those subproblems
solved by common ants. Three MoACO algorithms designed by, respectively, combining MoACO/D with AS, MMAS and ACS are presented.
Extensive experiments conducted on ten bTSPs with various complexities manifest that MoACO/D is both efficient and effective
for solving bTSPs and the ACS version of MoACO/D outperforms three well-known MoACO algorithms on large bTSPs according to
several performance measures and median attainment surfaces.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-18
  • DOI 10.1007/s00500-011-0759-3
  • Authors
    • Jixang Cheng, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031 China
    • Gexiang Zhang, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031 China
    • Zhidan Li, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031 China
    • Yuquan Li, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031 China

A note on double lacunary statistical -convergence of fuzzy numbers

Abstract  Quite recently, Savaş (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence

X={Xk,l}
of fuzzy numbers as follows: a double sequence

X={Xk,l}
is said to be lacunary P-statistically …

Abstract  

Quite recently, Savaş (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence

X={Xk,l}

of fuzzy numbers as follows: a double sequence

X={Xk,l}

is said to be lacunary P-statistically convergent to

X0

provided that for each $$\epsilon >0$$


P–
lim
r,s 		 \frac1hr,s| {(k,l) Î Ir,s: d(Xk,l ,X0) ³ e}| = 0.

In this paper we introduce and study double lacunary

s

-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-5
  • DOI 10.1007/s00500-011-0757-5
  • Authors
    • Ekrem Savaş, Department of Mathematics, Istanbul Commerce University, Üsküdar, Istanbul, Turkey

Metaheuristic optimization frameworks: a survey and benchmarking

Abstract  This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison
a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include t…

Abstract  

This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison
a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic
techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel
and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been
defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to
a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined
filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps
in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics,
and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software
Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-35
  • DOI 10.1007/s00500-011-0754-8
  • Authors
    • José Antonio Parejo, University of Sevilla, Seville, Spain
    • Antonio Ruiz-Cortés, University of Sevilla, Seville, Spain
    • Sebastián Lozano, University of Sevilla, Seville, Spain
    • Pablo Fernandez, University of Sevilla, Seville, Spain