FUZZY COMPARATIVE CONCORDANCE ANALYSIS. Proposal and evaluation by a case study

In this paper it is proposed a fuzzy multiple attribute analysis, that we have called comparative concordance, as a help instrument to the decision-making process in an environment of lack of precise information as it generally is the decision-making in regional planning. Through an application to the selection of proceeding programs of the Environmental Plan of Andalusia, 1995-2000, it will be compared to other methods.fuzzy sets, multiple attribute decision, environmental planning

Fuzzy set applications in engineering optimization: Multilevel fuzzy optimization

A formulation for multilevel optimization with fuzzy objective functions is presented. With few exceptions, formulations for fuzzy optimization have dealt with a one-level problem in which the objective is the membership function of a fuzzy set formed by the fuzzy intersection of other sets. In the problem examined here, the goal set G is defined in a more general way, using an aggregation operator H that allows arbitrary combinations of set operations (union, intersection, addition) on the individual sets Gi. This is a straightforward extension of the standard form, but one that makes possible the modeling of interesting evaluation strategies. A second, more important departure from the standard form will be the construction of a multilevel problem analogous to the design decomposition problem in optimization. This arrangement facilitates the simulation of a system design process in which different components of the system are designed by different teams, and different levels of design detail become relevant at different time stages in the process: global design features early, local features later in the process

Modelling and optimizing multiple attribute decisions by using fuzzy sets

The purpose of this paper is to present a coherent perspective of modeling and optimizing multiple attribute decisions by using fuzzy sets. In management practice we face most of the time the situation in which a problem have several possible solutions and each solution can be analyzed using multiple criteria models. In the same time, in real life decision making process there is a given level of uncertainty which makes difficult a clear cut analytical analysis. The object of this article is to build a model approach for making multiple criteria decision using fuzzy sets of objects. Elaborating multiple attribute decisions involves performing an assessment and selecting from a given and finite set of possible alternative courses of action in the presence of a given and finite, and usually conflicting set of attributes and criteria.decision making, fuzzy sets, modeling, multiple criteria optimization.

Fuzzy Subset Theory in the Measurement of Poverty

What has not been explored in the traditional measures of poverty is the extensive set of categorical variables that indicate standard of living and are already available from existing survey data. What precluded researchers from deriving poverty and welfare gauges from these data is the difficulty of incorporating these indicators in their measurement. This article offers a new approach to the traditional measures that allow these available data to be readily utilized. While accounting for the multi-dimensionality of the poverty phenomenon, the approach still provides formalism in the use of other variable parallel to and complementary with income and expenditure.poverty, econometric modeling, data and statistics

A new fuzzy algorithm for ecological ranking

Ecological ranking is a prerequisite to many kinds of environmental decisions. It requires a set of 'objects' (e.g., competing sites for species reintroduction, or competing alternatives of environmental management) to be evaluated on the basis of multiple weighted criteria, and then ranked from the best to the worst, or vice versa. The resulting ranking is then used to choose the course of an action (e.g., the optimal sites where a species can be reintroduced, or the optimal management scenario for a protected area). In this work, a new tool called FuzRnk is proposed as a modification of classic fuzzy algorithm. FuzRnk, which is freely available upon request from the author, allows for a fuzzy ranking of GIS objects (e.g., landscape patches or zones within protected areas). With respect to classic fuzzy algorithm, FuzRnk introduces two modifications: a) criteria can be weighted on the basis of their importance, b) not only the best performances, but also the worst ones are considered in the ranking procedure

Digital Image Classication of Land Cover.

Classication of land cover represents a quite standard example of natural fuzzy boundaries between phenomena: many classes show in nature internal gradual diferences in species, health, age, moisture, as well other factors. It is unrealistic to establish a line between diferent classes, assuming homogeneous diferent phenomena in each side. In this paper we consider the unsupervised algorithm presented in Amo et al. [2], applied to a real image in Sevilla province (south Spain). As expected, image is easier to be understood taking into account few fuzzy classes and all their transition zones, rather than assuming a large family of crisp classes showing no structure between those classes

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

"Can Banks Learn to Be Rational?"

Can banks learn to be rational in their lending activities? The answer depends on the institutionally bounded constraints to learning. From an evolutionary perspective the functionality (for survival) of "learning to be rational" creates strong incentives for such learning without, however, guaranteeing that each member of the particular economic species actually achieves increased fitness. I investigate this issue for a particular economic species, namely, commrercial banks. The purpose of this paper is to illustrate the key issues related to learning in an economic model by proposing a new screening model for bank commercial loans that uses the neuro fuzzy technique. The technical modeling aspect is integrally connected in a rigorous way to the key conceptual and theoretical aspects of the capabilities for learning to be rational in a broad but precise sense. This paper also compares the relative predictability of loan default among three methods of prediction--- discriminant analysis, logit type regression, and neuro fuzzy--- based on the real data obtained from one of the banks in Taiwan.The neuro fuzzy model, in contrast with the other two, incorporates recursive learning in a real world, imprecise linguistic environment. The empirical results show that in addition to its better screening ability, the neuro fuzzy model is superior in explaining the relationship among the variables as well. With further modifications,this model could be used by bank regulatory agencies for loan examination and by bank loan officers for loan review. The main theoretical conclusion to draw from this demonstration is that non-linear learning in a vague semantic world is both possible and useful. Therefore the search for alternatives to the full neoclassical rationality and its equivalent under uncertainty---rational expectations--- is a plausible and desirable search, especially when the probability for convergence to a rational expectations equilibrium is low.

Multi-Index Bi-Criterion Transportation Problem: A Fuzzy Approach

This paper represents a non linear bi-criterion generalized multi-index transportation problem (BGMTP) is considered. The generalized transportation problem (GTP) arises in many real-life applications. It has the form of a classical transportation problem, with the additional assumption that the quantities of goods change during the transportation process. Here the fuzzy constraints are used in the demand and in the budget. An efficient new solution procedure is developed keeping the budget as the first priority. All efficient time-cost trade-off pairs are obtained. D1-distance is calculated to each trade-off pair from the ideal solution. Finally optimum solution is reached by using D1-distance

Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm

The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology [7] can be improved by applying a fuzzy classification algorithm [2] based on a fuzzy outranking methodology [9] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes