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This paper gives an overview on some classes of binary connectives that play a key role in fuzzy logic. We start with left-continuous triangular norms, by recalling some facts about the nilpotent minimum and its extensions. Some standard construction methods are also described. We also deal with two possible extensions of t-norms: the classes of uninorms and nullnorms.
We expand the classical model of a two-player game to select the best strategies, whose action is expected to maintain the values of a certain variable on the neutral level. By inserting fuzzy sets as payoff values in the game matrix we facilitate the procedure of formulations of payoff expectations by players. Instead of making difficult decisions about the choice of accurate numerical entries of...
Value at risk (VaR) is a measure for senior management that summarises the financial risk a company faces into one single number. In this paper, we consider the use of fuzzy histograms for quantifying the value-at-risk of a portfolio. It is shown that the use of fuzzy histograms provides a good method of value-at-risk estimation for a portfolio of stocks. The conditional parameters of the model are...
Hybrid models combine different technologies to obtain a product that shares their advantages and minimizes their deficiencies. The solutions given by a case-based system (CBS) rely on similar past experiences, which are commonly described in terms of both symbolic and continuous attributes. The nearest neighbor (NN) principle commonly followed to develop CBS for classification task proceeds from...
The idea of linguistic variable in fuzzy logic allows the representation of the subjective meaning of a measure at a given instant of time. This paper explores possibilities of extending the concept of linguistic term to create the linguistic description of the temporal evolution of a signal. We focus our effort in signals that, having potentially an infinite duration, present more or less repetitive...
This work presents a hybrid fuzzy modeling approach based on the conditional fuzzy clustering algorithm, that aims to provide new means to handle the issue of interpretability of the rule base. The balance between interpretability and accuracy of fuzzy rules is addressed by means of the definition of contexts formed with a small number of input variables and the generation of clusters conditioned...
A series of experiments aimed to generate and learn fuzzy models for the valuation of residential premises was conducted using the KEEL tool (knowledge extraction based on evolutionary learning). Four regression and four post-processing algorithms were applied to several data sets. They referred to sales/purchase transactions of residential premises, which were derived from the cadastral system and...
This paper presents a hybrid optimization method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the members of the harmony memory in the HS method. The hybrid optimization algorithm is further used to optimize a fuzzy classification system for the Fisher Iris data classification. Computer simulations results demonstrate...
Multiobjective genetic fuzzy systems (MGFSs) have proved to be very effective in classification, regression and control tasks. However, large scale problems still present open and challenging research issues. Making identification of fuzzy rules faster can enlarge the range of applications of MGFSs. In this work we first analyze the time complexity for both the identification and the evaluation of...
In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify...
In this paper we propose a two-level MOEA to help on the sugarcane harvest decision support. This problem is multi-objective in nature, as it contains agronomical and logistic objectives considered simultaneously. Two different sets of heuristics were used during harvest decisions, namely crisp and fuzzy prioritization schema. They are both tested and compared here with regards to effective help to...
This paper introduces a weighted partitioning dynamic clustering algorithm for quantitative feature data based on adaptive euclidean distances. The proposed method is an iterative four-steps relocation algorithm involving the determination of the clusters representatives (prototypes), the weight of each individual, the distance associated to each cluster and the construction of the clusters, at each...
In this work our aim is to increase the performance of fuzzy rule based classifications systems in the framework of imbalanced data-sets by means of the application of a genetic tuning step. We focus on the imbalanced data-set problem since it appears in many real application areas and, for this reason, it has become a relevant topic in the area of machine learning. This problem occurs when the number...
In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs...
The dimension of a knowledge domain can impact the use of genetic algorithms to automatically design fuzzy rule bases, since the search space for the genetic algorithm increases exponentially with the number of features. Filters are a possible approach to reduce the number of features. However, the filter approach does not take into consideration the particular aspects of fuzzy logic when selecting...
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images. The algorithm is based on a hybrid technique composed of simulated annealing and differential evolution. A new fuzzy objective function has been derived for the edge map of a given image. Minimization of this function with a hybrid annealed differential evolution algorithm leads to...
Neural network is a widely used and an effective artificial intelligence technique used for predictions and classifications which has been developed based on human biological neural system. Determining the structure of a neural network is a very complex task and there is no defined approach to determine the structure, especially the number of hidden nodes. Traditionally the number of hidden nodes...
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