scholarly journals The Performance of Stock Portfolios formed using Fuzzy Logic

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.

scholarly journals Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 189 ◽  
Author(s):  
Aryuanto Soetedjo ◽  
Yusuf Nakhoda ◽  
Choirul Saleh
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Cost Reduction ◽  
Fuzzy Logic Controller ◽  
Management Systems ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Energy Management Systems ◽  
Fuzzy Membership Functions ◽  
Home Energy Management

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

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

2005 ◽  
Vol 9 (5) ◽  
pp. 534-539 ◽  
Author(s):  
Kalle Saastamoinen ◽  
◽  
Jaakko Ketola
Keyword(s):  
Expert System ◽  
Differential Evolution ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Significant Difference ◽  
Modification Factor ◽  
Generalized Mean ◽  
Aerobic And Anaerobic ◽  
The Membership Function

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.

scholarly journals Using Artificial Intelligence to Predict Class Loyalty and Plagiarism in Students in an Online Blended Programming Course during the COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2203
Author(s):  
Jain-Shing Wu ◽  
Ting-Hsuan Chien ◽  
Li-Ren Chien ◽  
Chin-Yi Yang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Membership Function ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
High Class ◽  
Fuzzy Membership Functions ◽  
Novel Method ◽  
Programming Courses

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent.

Sistem Pakar Deteksi Kerusakan Truk Mitsubishi Fuso Berbasis Desktop

2021 ◽  
Vol 11 (1) ◽  
pp. 1-4
Author(s):  
Heri Pratama ◽  
Sofika Enggari ◽  
Irzal Arief Wisky
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Computer Program ◽  
Specific Problem ◽  
Expert Knowledge ◽  
Computer Programs ◽  
Thought Process ◽  
Made In

An expert system is a computer program that can mimic the thought process and expert knowledge in solving a particular problem. The implementation of this expert system is widely used in the field of artificial intelligence because expert systems are seen as a way of storing expert knowledge in certain fields in computer programs so that decisions can be made in making intelligent reasoning on a specific problem in this case the problem of detecting damage to Mitsubishi trucks. Fuso at Berdikari Motor Sibolga workshop.

scholarly journals SUM NORMAL OPTIMIZATION OF FUZZY MEMBERSHIP FUNCTIONS

2002 ◽  
Vol 10 (04) ◽  
pp. 363-384 ◽  
Author(s):  
DAN SIMON
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Optimization Problem ◽  
Fuzzy Membership ◽  
Optimization Approach ◽  
Membership Functions ◽  
Fuzzy Logic Systems ◽  
Fuzzy Membership Functions ◽  
Normal Constraint ◽  
Logic System

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This optimization approach is modified in this paper so that the resulting membership functions are sum normal. Sum normality is desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The sum normal constraint is applied in this paper to both gradient descent optimization and Kalman filter optimization of fuzzy membership functions. The methods are illustrated on a fuzzy automotive cruise controller.

scholarly journals Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Salisu Muhammad Sani
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Problem Solving ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Membership ◽  
Linguistic Variables ◽  
Membership Functions ◽  
Fuzzy Membership Functions

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

Expert Systems and Fuzzy Logic

2018 ◽  
pp. 48-75
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Intelligent System ◽  
Optimal Solution ◽  
Human Experience ◽  
Probabilistic Methods ◽  
Approximate Methods ◽  
Rules Of Thumb

An expert system is a system that employs human experience or knowledge captured in a computer to solve problems that ordinarily require human expertise. They may or may not have a learning component. Expert systems are a branch of Artificial intelligence. Truemper describes an expert system as an intelligent system which in an interactive setting asks a person for information and, based upon the response, draws conclusions or gives advice. Problems tend to be solved using heuristics (rules of thumb) or approximate methods or probabilistic methods which, unlike algorithmic solutions, are not guaranteed to result in a correct or optimal solution. The authors go further to clarify that expert systems usually have to provide explanations and justifications of their solutions or recommendations in order to convince the user that their reasoning is correct.

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