763 research outputs found
Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft
In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor
Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems
Nowadays, there is a increasing in the use of learning management systems
from the universities. This type of systems are also known under other
di erent terms as course management systems or learning content management
systems. Speci cally, these systems are e-learning platforms o ering
di erent facilities for information sharing and communication between the
participants in the e-learning process.
This contribution presents an experimental study with several subgroup
discovery algorithms based on evolutionary fuzzy systems using data from a
web-based education system. The main objective of this contribution is to
extract unusual subgroups to describe possible relationships between the use
of the e-learning platform and marks obtained by the students. The results
obtained by the best performing algorithm, NMEEF-SD, are also presented.
The most representative results obtained by this algorithm are summarised in
order to obtain knowledge that can allow teachers to take actions to improve student performance
Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing
This paper presents a multiobjective genetic algorithm which obtains
fuzzy rules for subgroup discovery in disjunctive normal form. This kind of
fuzzy rules lets us represent knowledge about patterns of interest in an
explanatory and understandable form which can be used by the expert. The
evolutionary algorithm follows a multiobjective approach in order to optimize
in a suitable way the different quality measures used in this kind of problems.
Experimental evaluation of the algorithm, applying it to a market problem
studied in the University of Mondragón (Spain), shows the validity of the
proposal. The application of the proposal to this problem allows us to obtain
novel and valuable knowledge for the experts.Spanish Ministry of Science and TechnologyFEDER TIC-2005-08386-C05-01 and TIC-2005-
08386-C05-03TIN2004-20061-E and TIN2004-21343-
Multiobjective programming for type-2 hierarchical fuzzy inference trees
This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an
optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by
combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure
provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases.
Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a
simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a
different input’s combination, where the evolutionary process governs the input’s combination. Hence,
HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated
by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP
for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in
data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared
both theoretically and empirically with recently proposed FISs methods from the literature, such as
McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly accurate models compared
to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and
competitive alternative to the other FISs for function approximation and feature selectio
Subgroup Discovery: Real-World Applications
Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive
and descriptive induction. In this paper, an overview about subgroup discovery is performed.
In addition, di erent real-world applications solved through evolutionary algorithms where the
suitability and potential of this type of algorithms for the development of subgroup discovery
algorithms are presented
Subgroup Discovery trhough Evolutionary Fuzzy Systems applied to Bioinformatic problems
Subgroup discovery is a descriptive data mining technique using supervised learning. This
paper presents a summary about the main properties and elements about subgroup discovery task.
In addition, we will focus on the suitability and potential of the search performed by evolutionary
algorithms in order to apply in the development of subgroup discovery algorithms, and in the use
of fuzzy logic which is a soft computing technique very close to the human reasoning. The
hybridisation of both techniques are well known as evolutionary fuzzy system.
The most relevant applications of evolutionary fuzzy systems for subgroup discovery in the
bioinformatics domains are outlined in this work. Specifically, these algorithms are applied to a
problem based on the Influenza A virus and the accute sore throat problem
Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective
Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods
Application of AI in Chemical Engineering
A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields
A Hybrid Grey Relational Analysis and Nondominated Sorting Genetic Algorithm-II for Project Portfolio Selection
Project selection and formation of an optimal portfolio of selected projects are among the main challenges of project management. For this purpose, several factors and indicators are simultaneously examined considering the terms and conditions of the decision problem. Obviously, both qualitative and quantitative factors may influence the formation of a portfolio of projects. In this study, the projects were first ranked using grey relational analysis to form an optimal portfolio of projects and to create an expert system for the final project selection. Because of the fuzzy nature of the environmental risk of each project, the environmental risk was predicted and analyzed using the fuzzy inference system and failure mode and effect analysis based on fuzzy rules. Then, the rank and risk of each project were optimized using a two-objective zero-one mathematical programming model considering the practical constraints of the decision problem through the nondominated sorting genetic algorithm-II (NSGA-II). A case study was used to discuss the practical methodology for selecting a portfolio of projects
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