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Currently, many existing information systems contain large amounts of data. Prior research shows that many of these data represent time (domain) intervals and many of those may be subject to uncertainty.
Feature selection is a dimensionality reduction technique with the purpose of selecting those input features that are most predictive of a given outcome. The benefits of feature selection include simplified feature internal-relationship and improved prediction performance. Methods based on fuzzy-rough set theory have employed the dependency function to guide the feature selection process with much...
Fuzzy Answer Set Programming (FASP) extends the popular Answer Set Programming (ASP) paradigm to modeling and solving combinatorial search problems in continuous domains. The recent development of FASP solvers has turned FASP into a practical tool for solving real-world problems. In this paper, we propose the use of FASP for modeling the dynamics of Gene Regulatory Networks (GRNs), an important kind...
The research work carried out in this paper presents a novel intelligent tool condition monitoring solution for the turning process using an enhanced adaptive neural fuzzy inference system based on extended subtractive clustering. The hybrid system is constructed from training a takagi-sugeno-kang fuzzy logic system by integrating machining parameters- feed, cutting force, feed force and cutting tool...
Nowadays, many research works are moving toward the definition of models for human decision support systems within business process executions. Existing solutions, in general, do not take into account the context in which such processes run but they provide rigid models that could erroneously support decision-making activities when a different context needs to be considered. This work focuses on the...
In this paper, an adaptive fuzzy controller design methodology based on robust stability linguistic criterion and multi-objective particle swarm optimization, for non-linear and time-variant systems, is proposed. The robust stability linguistic specifications (gain and phase margins) are extracted from the expert knowledge and represented mathematically using the fuzzy numbers theory. The controller...
In this paper, the fundamental idea of Incremental Granular Models (IGM) introduced by Pedrycz and Kwak (2007) is followed and their comprehensive design framework is developed. In contrast to typical rule-based systems encountered in fuzzy modeling, the underlying principle of IGM is to consider a two-phase development. First, we build a Linear Regression (LR) model which could be treated as a global...
This paper proposes a new metaheuristic population-based evolutionary optimization algorithm, mutation-aided elite continuous ant colony optimization (MECACO), for the design of TSK-type recurrent fuzzy neural network (TRFN). The basic principle of MECACO is a stochastic search algorithm which combines a new designed elites-based continuous ACO with the mutation technique employing the dynamic mutation...
This paper provides a mathematical analysis that shows how the crisp output of an IT2 FLS that is obtained by using the Begian-Melek-Mendel (BMM) formula compares to the one obtained by using center-of-sets type-reduction followed by defuzzification (COS TR + D). This is made possible by reformulating the structural solutions of the two optimization problems that are associated with COS TR, and then...
One challenge in recommender system is to deal with data sparsity. To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. The most intuitive way is to use common tags that present in both source and target domains. However, it is difficult to obtain a strong domain connection by exploiting a small amount of common tags,...
This paper proposes a fast load pattern extraction approach to solve the time consuming problem in using a traditional k-means clustering method for large volumes of load curves. The approach, based on dimension reduction and sampling, segments and averages sampling characteristic points to reduce the load curve's dimensions, then reduces the overall size of the sample data set using representative...
This paper aims to present a systematic methodology for designing a Replica of an Advanced Takagi-Sugeno (RATS) discrete observer. Advance observers for nonlinear systems under Takagi-Sugeno representation have been designed for years using efficient structure such as non-PDC (Parallel Distributed Compensation), which it is powerful and, in some cases, it is the only observer able to estimate an unknown...
The paper presents an approach to designing from data fuzzy decision systems (fuzzy rule-based classifiers (FRBCs)) by means of four multi-objective evolutionary optimization algorithms (MOEOAs) including the well-known NSGA-II, ϵ-NSGA-II, SPEA2, and our generalization of SPEA2 (referred to as SPEA3). The advantages of SPEA3 (a better-balanced distribution and a higher spread of solutions than for...
Kriging or Gaussian Process Regression has been successfully applied in many fields. One of the major bottlenecks of Kriging is the complexity in both processing time (cubic) and memory (quadratic) in the number of data points. To overcome these limitations, a variety of approximation algorithms have been proposed. One of these approximation algorithms is Optimally Weighted Cluster Kriging (OWCK)...
Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group of semi-supervised classification approaches are those known as self-labelling techniques, which attempt to assign class labels to the unlabelled data instances. This is...
In some cases, a membership function μ(x) represents an unknown number, but in many other cases, it represents an unknown crisp set. In this case, for each crisp set S, we can estimate the degree μ(S) to which this set S is the desired one. A natural question is: once we know the values μ(S) corresponding to all possible crisp sets S, can we reconstruct the original membership function? In this paper,...
Sports video summarization and classification is becoming a very important topic due to the pressing need to automatically classify sports scenes to enable better sport analysis, refereeing, training and advertisement. The vast majority of the techniques applied to sports video classifications involved black box techniques such as support vector machines (SVMs) and neural networks, which do not provide...
This paper presents an idea to further simplify and relax the linear matrix inequality (LMI) stability conditions of Takagi-Sugeno (T-S) fuzzy systems. By considering the distribution of membership functions in a unified space, we can easily find the overall relation of the original nonlinear system and its approximated local subsystems. Based on the theory of convex combination, the upper-bounds...
A time series is the most commonly used representation for the evolution of a given variable over time. In a time series forecasting problem, a model aims at predicting the series' future values, assuming that all information needed to do so is contained in the series' past behavior. Since the phenomena described by the time series does not always exist in isolation, it is possible to enhance the...
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