12,259 research outputs found
Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition
Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo
A Formal Approach based on Fuzzy Logic for the Specification of Component-Based Interactive Systems
Formal methods are widely recognized as a powerful engineering method for the
specification, simulation, development, and verification of distributed
interactive systems. However, most formal methods rely on a two-valued logic,
and are therefore limited to the axioms of that logic: a specification is valid
or invalid, component behavior is realizable or not, safety properties hold or
are violated, systems are available or unavailable. Especially when the problem
domain entails uncertainty, impreciseness, and vagueness, the appliance of such
methods becomes a challenging task. In order to overcome the limitations
resulting from the strict modus operandi of formal methods, the main objective
of this work is to relax the boolean notion of formal specifications by using
fuzzy logic. The present approach is based on Focus theory, a model-based and
strictly formal method for componentbased interactive systems. The contribution
of this work is twofold: i) we introduce a specification technique based on
fuzzy logic which can be used on top of Focus to develop formal specifications
in a qualitative fashion; ii) we partially extend Focus theory to a fuzzy one
which allows the specification of fuzzy components and fuzzy interactions.
While the former provides a methodology for approximating I/O behaviors under
imprecision, the latter enables to capture a more quantitative view of
specification properties such as realizability.Comment: In Proceedings FESCA 2015, arXiv:1503.0437
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
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