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1.
《Advances in Engineering Software》2003,34(3):125-137
An approach for an effective and efficient off-line training of particular classes of reusable controller software components is presented. To build a necessary relationship between a component's abstract and concrete levels, each control software component is represented at the abstract level by means of a set of adaptive fuzzy logic rules and at the concrete level by means of adaptive fuzzy membership functions. Training includes two phases: testing and adapting. The testing phase is for identifying faulty fuzzy elements of a component, while the adapting phase is for modifying membership functions. We employ genetic algorithms, neural network algorithms, Monte Carlo algorithms, and their combinations in each phase. This approach is illustrated by training automotive controller software components (simulation). Experimental simulation results show that our off-line training approach supports controller software component adaptation effectively and efficiently in terms of controlled process operation accuracy and effort spent. 相似文献
2.
Operator and parameter adaptation in genetic algorithms 总被引:6,自引:1,他引:6
J. E. Smith T. C. Fogarty 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1997,1(2):81-87
Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor
of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the
“population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which
has a scalar value attached to it reflecting its quality or “fitness”. The search may be seen as the iterative application
of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively
fitter individuals.
These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed
at the beginning and constant throughout the run of the algorithm. However, there is an increasing body of evidence that not
only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators
for a given problem will be time-variant i.e. it will depend on such factors as the degree of convergence of the population.
Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or
more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improve the performance
of the Genetic Algorithm as a function optimiser.
In this paper we describe the background to these approaches, and suggest a framework for their classification, based on the
learning strategy used to control them, and what facets of the algorithm are susceptible to adaptation. We then review a number
of significant pieces of work within the context of this setting, and draw some conclusions about the relative merits of various
approaches and promising directions for future work. 相似文献
3.
ContextThe software defect prediction during software development has recently attracted the attention of many researchers. The software defect density indicator prediction in each phase of software development life cycle (SDLC) is desirable for developing a reliable software product. Software defect prediction at the end of testing phase may not be more beneficial because the changes need to be performed in the previous phases of SDLC may require huge amount of money and effort to be spent in order to achieve target software quality. Therefore, phase-wise software defect density indicator prediction model is of great importance.ObjectiveIn this paper, a fuzzy logic based phase-wise software defect prediction model is proposed using the top most reliability relevant metrics of the each phase of the SDLC.MethodIn the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using nine software metrics of these four phases. The defect density indicator metric predicted at the end of the each phase is also taken as an input to the next phase. Software metrics are assessed in linguistic terms and fuzzy inference system has been employed to develop the model.ResultsThe predictive accuracy of the proposed model is validated using twenty real software project data. Validation results are satisfactory. Measures based on the mean magnitude of relative error and balanced mean magnitude of relative error decrease significantly as the software project size increases.ConclusionIn this paper, a fuzzy logic based model is proposed for predicting software defect density indicator at each phase of the SDLC. The predicted defects of twenty different software projects are found very near to the actual defects detected during testing. The predicted defect density indicators are very helpful to analyze the defect severity in different artifacts of SDLC of a software project. 相似文献
4.
P. Cintula 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2001,5(3):243-244
This short paper has two goals. The first is to show a new axiomatic system of product fuzzy logic with only one non-BL axiom
which has only two variables. The second goal is to prove that there cannot be any axiomatic system of the product fuzzy logic
with single non-BL axiom with only one variable. 相似文献
5.
Ing-Jr Ding Author Vitae 《Pattern recognition》2007,40(11):3110-3119
This paper presents a fuzzy control mechanism for conventional maximum likelihood linear regression (MLLR) speaker adaptation, called FLC-MLLR, by which the effect of MLLR adaptation is regulated according to the availability of adaptation data in such a way that the advantage of MLLR adaptation could be fully exploited when the training data are sufficient, or the consequence of poor MLLR adaptation would be restrained otherwise. The robustness of MLLR adaptation against data scarcity is thus ensured. The proposed mechanism is conceptually simple and computationally inexpensive and effective; the experiments in recognition rate show that FLC-MLLR outperforms standard MLLR especially when encountering data insufficiency and performs better than MAPLR at much less computing cost. 相似文献
6.
Javier Aroba Author Vitae Juan J. Cuadrado-Gallego Author Vitae 《Journal of Systems and Software》2008,81(11):1944-1950
Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees.This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart. 相似文献
7.
8.
K. Saravana Kumar Ravindra Babu Misra 《国际自动化与计算杂志》2007,4(4):388-395
Software operational profile (SOP) is used in software reliability prediction,software quality assessment,performance analysis of software,test case allocation,determination of"when to stop testing,"etc.Due to the limited data resources and large efforts required to collect and convert the gathered data into point estimates,reluctance is observed by the software professionals to develop the SOP.A framework is proposed to develop SOP using fuzzy logic,which requires usage data in the form of linguistics.The resulting profile is named fuzzy software operational profile (FSOP).Based on this work,this paper proposes a generalized approach for the allocation of test cases,in which occurrence probability of operations obtained from FSOP are combined with the criticality of the operations using fuzzy inference system (FIS).Traditional methods for the allocation of test cases do not consider the application in which software operates.This is intuitively incorrect.To solve this problem,allocation of test cases with respect to software application using the FIS model is also proposed in this paper. 相似文献
9.
M. Feroldi 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2000,4(4):224-236
Here is introduced an application of the Genetic and Evolutive Algorithms to the Unit Commitment Problem. It is a mixed integer
problem of constrained non linear combinatorial optimization. The many constraints make the problem very complex.
Three cases of study on the problem have been faced, characterized by crescent grades of completeness/ difficulties in order
to understand which are the advantages and the difficulties which arise from the evolutive approach. In the cases of study
have been faced dimensions of the problem significant in practice: from 10 up to 1000 generators. 相似文献
10.
Peizhuang Wang Shaohua Tan 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1997,1(1):35-41
What is soft computing? What is fuzzy computing? What is the relationship between them? This paper intends to provide clear answers to these questions. We focus on exploring the notions of the fuzzy coordinate system and the related transformations between qualitative and quantitative information. These notions are considered to be the core ideas of fuzzy computing. Then the three novel theories of fuzzy computing and soft computing developed by the first author of this paper, namely, the Falling Shadow Representation of fuzzy theory, the Factors Space theory and the Truth Value Flow Inference theory are introduced. 相似文献
11.
Search based software testing of object-oriented containers 总被引:1,自引:0,他引:1
Automatic software testing tools are still far from ideal for real world object-oriented (OO) software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given. 相似文献
12.
The uses of fuzzy logic in autonomous robot navigation 总被引:10,自引:0,他引:10
A. Saffiotti 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1997,1(4):180-197
The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in
the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount
of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address
this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We
focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules;
how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review
some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions.
Received: 31 March 1997 / Accepted: 24 September 1997 相似文献
13.
R. Bělohlávek 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1999,3(1):37-43
The paper discusses feedforward neural networks with fuzzy signals. We analyze the feedforward phase and show some properties
of the output function. Then we present a backpropagation like adaptation algorithm for crisp weights, thresholds and neuron
slopes of the multilayer network with sigmoidal transfer functions. We provide theoretical justification for the adaptation
formulas. The results are of general nature and together with the presented approach can be used for other types of feedforward
networks. Proposed and discussed are also applications of the presented feedforward networks. 相似文献
14.
O. Cordón F. Moya C. Zarco 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2002,6(5):308-319
Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits
a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user
original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar
to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the
retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the
vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a
lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution
we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary
algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will
be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal
outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation
of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased. 相似文献
15.
Adaptive mutation in genetic algorithms 总被引:1,自引:0,他引:1
S. Marsili Libelli P. Alba 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2000,4(2):76-80
In Genetic Algorithms mutation probability is usually assigned a constant value, therefore all chromosome have the same likelihood
of mutation irrespective of their fitness. It is shown in this paper that making mutation a function of fitness produces a
more efficient search. This function is such that the least significant bits are more likely to be mutated in high-fitness
chromosomes, thus improving their accuracy, whereas low-fitness chromosomes have an increased probability of mutation, enhancing
their role in the search. In this way, the chance of disrupting a high-fitness chromosome is decreased and the exploratory
role of low-fitness chromosomes is best exploited. The implications of this new mutation scheme are assessed with the aid
of numerical examples. 相似文献
16.
V. Švejdar K. Bendová 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2000,4(2):103-105
In Gödel fuzzy propositional logic, neither conjunction nor implication is expressible (definable) in terms of the remaining three logical connectives. 相似文献
17.
G. Georgescu I. Leuştean 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2000,4(1):19-26
In this paper a concept of probability defined on a Lukasiewicz-Moisil algebra is proposed. We take some steps in developing
the theory, including an extension theorem and some results related to conditional probabilities on Lukasiewicz-Moisil algebras. 相似文献
18.
J. J. Buckley T. Feuring Y. Hayashi 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2002,7(2):121-129
In this paper we are interested in generating good approximate solutions to single item, N-period, fuzzy inventory control problems. It is a fuzzy inventory control problem since some of the parameters (ordering
cost, holding cost, penalty cost) can be fuzzy numbers. We consider three cases: (1) demand is known each period; (2) demand
is unknown, and fuzzy each period; and (3) demand is fuzzy and backordering is allowed. We employ an evolutionary algorithm
to search out good approximate solutions. 相似文献
19.
J. J. Buckley T. Feuring Y. Hayashi 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2002,6(2):116-123
In this paper we use evolutionary algorithms and neural nets to solve fuzzy equations. In Part I we: (1) first introduce
our three solution methods for solving the fuzzy linear equation AˉXˉ + Bˉ= Cˉ; for Xˉ and (2) then survey the results for
the fuzzy quadratic equations, fuzzy differential equations, fuzzy difference equations, fuzzy partial differential equations,
systems of fuzzy linear equations, and fuzzy integral equations; and (3) apply an evolutionary algorithm to construct one
of the solution types for the fuzzy eigenvalue problem. In Part II we: (1) first discuss how to design and train a neural
net to solve AˉXˉ + Bˉ= Cˉ for Xˉ and (2) then survey the results for systems of fuzzy linear equations and the fuzzy quadratic. 相似文献
20.
R. A. Aliev B. Fazlollahi R. Vahidov 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2002,6(6):470-475
This paper describes the concept of fuzzy regression analysis based on genetic algorithms. It is shown that the performance
of fuzzy regression models may be improved and fuzzy modeling technique can be simplified by incorporating genetic algorithms
into regression analysis procedure. The effectiveness of the proposed approach is illustrated through simulation of fuzzy
linear regression model obtained by other authors and comparison of the results. The paper further demonstrates the applications
of the approach to the manufacturing and business problems. 相似文献