Soft computing: the convergence of emerging reasoning technologies

Bonissone, Piero P.

doi:10.1007/s005000050002

Cited by 205 publications

(82 citation statements)

References 84 publications

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“…CI is closely-related to the soft computing discipline. Quoting from Bonissone (Bonissone 1997) ''The term soft computing (SC) represents the combination of emerging problem-solving technologies such as fuzzy logic (FL), probabilistic reasoning (PR), neural networks (NNs), and genetic algorithms (GAs). Each of these technologies provides us with complementary reasoning and searching methods to solve complex, real-world problems''.…”

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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Section: Computational Intelligence Methodsmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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“…All these techniques are essentially heuristic which provide rational solutions for complex real-world problems (Bonissone, 1997). Quantitative aggregation of risk due to various sources is a complex process which warrants such an approach.…”

Section: Fuzzy Sets and Soft Computingmentioning

confidence: 99%

Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Khan

Sadiq

2005

Environ Monit Assess

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1007/s10661-005-3852-1Environmental Monitoring and Assessment, 105, 1-3, pp. 261-283, 2005-06-01 Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique Khan, F. I.; Sadiq, R. ABSTRACTAir pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritising the monitoring locations. The exposure-based approach prioritises the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritises the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), independently. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence).This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NO x , PM 10 and SO x are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritised based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.

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“…The age of Soft-computing (Zadeh, 1997;Bonissone, 1997) has introduced different kinds of hybrid systems, being the nineties the decade of neuro-fuzzy and genetic-fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Expert guided integration of induced knowledge into a fuzzy knowledge base

Guillaume¹,

Magdalena

2006

Soft Comput

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This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria: range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.

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Soft computing: the convergence of emerging reasoning technologies

Cited by 205 publications

References 84 publications

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Expert guided integration of induced knowledge into a fuzzy knowledge base

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