Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System

PB-ADVISOR: A private banking multi-investment porfolio.

Private banking is a business area in which the investor requires tailor-made advice. Because of the current market situation, investors are requiring answers to difficult questions and looking for assurance from wealth managers. Private bankers need to have deep knowledge about an innumerable list of products and their characteristics as well as the suitability of each product for the client’s characteristics to be able to offer an optimal portfolio according to client expectations. Client and portfolio diversity calls for new recommendation and advice systems focused on their specific characteristics. This paper presents PB-ADVISOR, a system aimed at recommending investment portfolios based on fuzzy and semantic technologies to private bankers. The proposed system provides private bankers with a powerful tool to support their decision process and help deal with complex investment portfolios. The system has been evaluated in a real scenario obtaining promising results

SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations

Portfolio selection addresses the problem of how to diversify investments in the most efficient and profitable way possible. Portfolio selection is a field of study that has been broached from several perspectives, including, among others, recommender systems. This paper presents SINVLIO (Semantic INVestment portfoLIO), a tool based on semantic technologies and fuzzy logic techniques that recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. The results are very encouraging and reveal that SINVLIO makes good recommendations, according to the high degree of agreement between SINVLIO and expert recommendationsThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the projects SONAR2 (TSI-020100-2008-665) and the Spanish Ministry of Science and Innovation under the project “FINANCIAL LINKED OPEN DATA REASONING AND MANAGEMENT FOR WEB SCIENCE” (TIN2011-27405).Publicad

A Knowledge Based System for Supporting Sustainable Industrial Management in a Clothes Manufacturing Company Based on a Data Fusion Model

In this paper we propose a knowledge based system (KBS), based on smart objects and a data fusion model to support industrial management decision making applied to a clothes manufacturing enterprise. The management processes cover factory-production levels to higher decision-making levels. Therefore, the proposed KBS contributes to solving different kind of decision problems, including factory supervision, production planning and control, productivity management, real-time monitoring, and data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms, decision methods, and smart objects, promote an optimized use of knowledge and resources. In this paper the proposed KBS is introduced and an example of its use is illustrated with an example of a clothes manufacturing resources selection, using the embedded dynamic multi-criteria fusion model.This work was supported by FCT “Fundação para a Ciência e a Tecnologia” under the program: PEST2015-2020, reference: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Proposing a new methodology for prioritising the investment strategies in the private sector of Iran

This article proposes a systematic and organised approach for group decision-making in the presence of the uncertainty involved in expert judgments as used in multi-criteria decision-making (MCDM) issues. This procedure comprises the selection of the optimum alternative with respect to the evaluation criteria under consideration, in particular to select the strategy of investing. However, the selection of the investment strategy is difficult on account of considering the numerous quantitative and qualitative parameters like benefits, opportunities, costs, and risks. However, it is possible that these parameters have a significant influence on each other. A decision-making trial and evaluation laboratory (DEMATEL), used to define the influential network of elements, can be employed to construct a network relationship map (NRM). On the other hand, according to whether the information is incomplete or unavailable, uncertainty is an inseparable part of making decision for solving the MCDM problems. Therefore, this article proposes a new hybrid model based on analytic hierarchical process (AHP), DEMATEL, and echnique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques under fuzzy environment to evaluate the problem of the selection of the investment strategy. To achieve the aim, a three-step process is presented to solve a sophisticated problem. First, the AHP method is employed to break down the investment problem into simple structure and calculate the importance weights of criteria by using a pairwise comparison process. Second, the DEMATEL technique is applied for considering interdependence and dependencies and computing the global weights of benefit, opportunities, cost, and risk (BOCR) factors. Finally, the fuzzy TOPSIS methodology is used for prioritising the possible alternatives. To demonstrate the potential application of the proposed model, a numerical example is illustrated and investigated. The results show that the proposed model has a high ability to prioritise the strategies of investing

Evaluating Capital Allocation Below Portfolio Level

This thesis explores the ability for retail banks to allocate economic capital below portfolio level. First, a discussion about capital requirements and risk measures to provide a sound basis for determining the economic capital of the bank. In general, economic capital is allocated to the banks portfolios but not on a more granular level, through a capital allocation method. This study discuss three dierent approaches for allocation of economic capital below portfolio level; game theory, nance and optimization. Both the game theory and nance approach reach the same conclusion, that the best allocation principle is the gradient of the risk measure. The optimization method allocates economic capital through minimization of a concept called risk residual, which conclude that the optimal allocation is derived from the marginal distribution of a customer. Capital allocation below portfolio level give the management a good overview of risks from dierent customers. In order to determine the performance of the portfolios in the bank a Risk-Adjusted-Return-On-Capital is used, with economic capital as input. The thesis include some comments about how the choice of capital allocation methods aect the performance measurement. The thesis concludes with an evaluation of the methods by simulations of a ctional bank conducted in the software R. Key Words: Risk Appetite, Economic Capital, Risk measure, Capital Allocation Methods, Allocation Below Portfolio level, Game theory, Optimization, Marginal Contributio

Updating the Lambda modes of a nuclear power reactor

[EN] Starting from a steady state configuration of a nuclear power reactor some situations arise in which the reactor configuration is perturbed. The Lambda modes are eigenfunctions associated with a given configuration of the reactor, which have successfully been used to describe unstable events in BWRs. To compute several eigenvalues and its corresponding eigenfunctions for a nuclear reactor is quite expensive from the computational point of view. Krylov subspace methods are efficient methods to compute the dominant Lambda modes associated with a given configuration of the reactor, but if the Lambda modes have to be computed for different perturbed configurations of the reactor more efficient methods can be used. In this paper, different methods for the updating Lambda modes problem will be proposed and compared by computing the dominant Lambda modes of different configurations associated with a Boron injection transient in a typical BWR reactor. (C) 2010 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia under projects ENE2008-02669 and MTM2007-64477-AR07, the Generalitat Valenciana under project ACOMP/2009/058, and the Universidad Politecnica de Valencia under project PAID-05-09-4285.González Pintor, S.; Ginestar Peiro, D.; Verdú Martín, GJ. (2011). Updating the Lambda modes of a nuclear power reactor. Mathematical and Computer Modelling. 54(7):1796-1801. https://doi.org/10.1016/j.mcm.2010.12.013S1796180154