Fuzzy Logic and Differential Evolution Based Expert System for Defining Top Athlete's Aerobic and Anaerobic Thresholds

This article describes an expert system for defining an athlete's aerobic and anaerobic thresholds that successfully mimics the decision-making done by sport medicine professionals. The functionality of our system is based on the fuzzy comparison measure, generalized mean, fuzzy membership functions and differential evolution. Differential evolution is used to tune the parameters in our comparison measure. This measure is based on the use of fuzzy equivalences and a modification factor that tunes the shape of the membership function in hand. The measure presented is especially suitable for expert systems. We will test our system in order to show that our result does not show any statistically significant difference from the values estimated by experts.

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The Performance of Stock Portfolios formed using Fuzzy Logic

Financial Statistical Journal ◽

10.24294/fsj.v1i2.814 ◽

2018 ◽

Vol 1 (2) ◽

Author(s):

Amit K. Sinha 1 ◽

Andrew J. Jacob 2

Keyword(s):

Artificial Intelligence ◽

Fuzzy Logic ◽

Expert System ◽

Expert Systems ◽

Fuzzy Membership ◽

Thought Process ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Stock Portfolios ◽

Financial Securities

Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.

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Fuzzy Multiobjective Optimization Used in Tolerance Synthesis of Mechanisms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.332.235 ◽

2013 ◽

Vol 332 ◽

pp. 235-240

Author(s):

Despina Duminică ◽

Iulian Dutu ◽

Mihai Avram ◽

Viorel Gheorghe

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Multiobjective Optimization ◽

Performance Criteria ◽

Fuzzy Membership ◽

Membership Functions ◽

Tolerance Intervals ◽

Fuzzy Membership Functions ◽

Synthesis Of Mechanisms ◽

The Membership Function

Fuzzy objective multioptimization describes performance criteria and constraints in terms of fuzzy membership functions. The membership function of the decision is obtained as conjunction of performance criteria and constraints. A method of allocating tolerance intervals using fuzzy multiobjective optimization is presented for the case of a 3R manipulator. The proposed method is validated through Monte Carlo simulation.

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A Reliability-Based Multisensor Data Fusion with Application in Target Classification

Sensors ◽

10.3390/s20082192 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2192

Author(s):

Gabriel Awogbami ◽

Abdollah Homaifar

Keyword(s):

Data Fusion ◽

Membership Function ◽

Fuzzy Membership ◽

Membership Functions ◽

Target Classification ◽

Decision Accuracy ◽

Multisensor Data Fusion ◽

Fuzzy Membership Functions ◽

Multisensor Data ◽

The Membership Function

The theory of belief functions has been extensively utilized in many practical applications involving decision making. One such application is the classification of target based on the pieces of information extracted from the individual attributes describing the target. Each piece of information is usually modeled as the basic probability assignment (BPA), also known as the mass function. The determination of the BPA has remained an open problem. Although fuzzy membership functions such as triangular and Gaussian functions have been widely used to model the likelihood estimation function based on the historical data, it has been observed that less emphasis has been placed on the impact of the spread of the membership function on the decision accuracy of the reasoning process. Conflict in the combination of BPAs may arise due to poor characterization of fuzzy membership functions to induce belief mass. In this work, we propose a multisensor data fusion within the framework of belief theory for target classification where shape/spread of the membership function is adjusted during the training/modeling stage to improve on the classification accuracy while removing the need for the computation of the credibility. To further enhance the performance of the proposed method, the reliability factor is deployed not only to effectively manage the possible conflict among participating bodies of evidence for better decision accuracy but also to reduce the number of sources for improved efficiency. The effectiveness of the proposed method was evaluated using both the real-world and the artificial datasets.

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Finding Topics in Email Using Formal Concept Analysis and Fuzzy Membership Functions

Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-540-68825-9_11 ◽

2008 ◽

pp. 108-113 ◽

Cited By ~ 1

Author(s):

Liqiang Geng ◽

Larry Korba ◽

Yunli Wang ◽

Xin Wang ◽

Yonghua You

Keyword(s):

Formal Concept Analysis ◽

Concept Analysis ◽

Fuzzy Membership ◽

Formal Concept ◽

Membership Functions ◽

Fuzzy Membership Functions

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00354-9 ◽

2021 ◽

Author(s):

Jia-Bin Zhou ◽

Yan-Qin Bai ◽

Yan-Ru Guo ◽

Hai-Xiang Lin

Keyword(s):

Support Vector Machine ◽

Negative Impact ◽

Twin Support Vector Machine ◽

Fuzzy Membership ◽

Support Vector ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Intuitionistic Fuzzy ◽

Benchmark Datasets ◽

The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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