scholarly journals APPLICATION OF NEURO-FUZZY MODELS IN THE SYSTEM FOR ASSESSING THE PROFESSIONAL ABILITIES OF APPLICANTS

2021 ◽  
pp. 107-113
Author(s):  
Yuliia Riabchun ◽  
Roman Skrypak ◽  
Olena Riabchun ◽  
Iryna Aznaurian
Keyword(s):  
Higher Education ◽  
Structural Model ◽  
Fuzzy Inference ◽  
Higher Education Institutions ◽  
A Priori ◽  
Education Institution ◽  
Inference System ◽  
Intellectual System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The work is devoted to solving a problem of assessing the professional abilities of entrants to higher education institutions. The subject of the study is the process of automatic support of entrants' decisions in conditions of fuzzy uncertainty caused by the need to communicate "online". The object of the study is a supporting means of the decisions of applicants to choose the direction of study "online". The main purpose of the work is to substantiate the technology of decision support for choosing a direction of study using an infocommunication system, the work of which has based on a neuro-fuzzy output system. Particular attention has paid to overcoming the problems that accompany the creation of infocommunication systems, which has designed to support decision-making on the choice of field of study in conditions of unclear uncertainty caused by the limitation of offline communication. The article presents the results of a study of the criteria for admission to higher education institutions in different countries. The structural model of the Specialized Intellectual System of Identification of Abilities of Entrants has offered. The system has designed to support the decision to choose a specialty of higher education institution. It has shown that to substantiate the recommendation conclusion based on the results of professional game; it is advisable to use a fuzzy neural network Takagi-Sugeno-Kanga. To solve the problem of substantiation of expert decisions at the stage of formation of a priori base of rules of fuzzy knowledge base of fuzzy inference system, it is expedient to use Mamdani model, which operates with linguistic variables and fuzzy sets.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

Hybrid Fuzzy Neural Search Retrieval System

2016 ◽  
Vol 12 (3) ◽  
pp. 78-93 ◽  
Author(s):  
Rawan Ghnemat ◽  
Adnan Shaout
Keyword(s):  
Fuzzy Inference System ◽  
Search Engines ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Experimental System ◽  
Search Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Face Search

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

A NOVEL ADAPTIVE NEURO-FUZZY UNSCENTED KALMAN FILTER FOR SLAM

2011 ◽  
Vol 08 (01) ◽  
pp. 223-243 ◽  
Author(s):  
RAMAZAN HAVANGI ◽  
MOHAMMAD TESHNEHLAB ◽  
MOHAMMAD ALI NEKOUI
Keyword(s):  
Kalman Filter ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
A Priori ◽  
Inference System ◽  
Localization And Mapping ◽  
Neuro Fuzzy ◽  
Measurement Noise Covariance ◽  
Slam Algorithm ◽  
Sampling Instant

Extended Kalman filter (EKF) has been used as a popular choice to solve simultaneous localization and mapping (SLAM) problem. However, SLAM algorithm based on EKF-SLAM has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobin matrices. For solving these problems, SLAM algorithm based on unscented Kalman filter (UKF-SLAM) has been recently proposed. However, the performance of the UKF-SLAM and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices respectively denoted by Qk and Rk. Imprecise knowledge of these statistics can cause significant degradation in performance. This article proposes the development of an adaptive neuro-fuzzy UKF (ANFUKF) for SLAM. The Adaptive neuro-fuzzy attempts to estimate the elements of Rk matrix in the UKF-SLAM algorithm at each sampling instant when measurement updating step is carried out. The adaptive neuro-fuzzy inference system (ANFIS) supervises the performance of the UKF-SLAM with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (GD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much as possible. The simulation results show the effectiveness of the proposed algorithm.

INSIGHT OF FUZZY NEURAL SYSTEMS IN THE APPLICATION OF HANDWRITTEN DIGITS CLASSIFICATION

2006 ◽  
Vol 06 (04) ◽  
pp. 511-532 ◽  
Author(s):  
GEOK SEE NG ◽  
SEVKI ERDOGAN ◽  
DAMING SHI ◽  
ABDUL WAHAB
Keyword(s):  
Character Recognition ◽  
Fuzzy Inference ◽  
Fuzzy Classification ◽  
Neural Systems ◽  
Practical Implementation ◽  
Inference System ◽  
Digit Recognition ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Handwritten Digit

There have been many applications in the area of handwritten character recognition. Over the last decade much research has gone into developing algorithms to accurately convert captured images of handwriting to text. At the same time, research into neuro fuzzy classification models has proven to solve many complex problems. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Network (EFuNN) was investigated and studied in detail on how these two models can be used to perform handwritten digits classification. Results of the experiments show great potential of the EFuNN over the ANFIS for practical implementation of the handwritten digit recognition.

scholarly journals THE IMAGE OF A HIGHER EDUCATION INSTITUTION, ITS STRUCTURE AND HIERARCHICAL LEVEL: THE CASE OF THE VILNIUS UNIVERSITY FACULTY OF ECONOMICS

Ekonomika ◽  
2010 ◽  
Vol 89 (3) ◽  
pp. 105-121 ◽  
Author(s):  
Olga Mackelo ◽  
Greta Drūteikienė
Keyword(s):  
Higher Education ◽  
Structural Model ◽  
Higher Education Institutions ◽  
Education Institution ◽  
Hierarchical Level ◽  
Study Quality ◽  
Student Survey ◽  
Higher Education Institution ◽  
Vilnius University ◽  
Constant Improvement

The aim of higher education institutions has been recently broadening, which in turn fostered the competitiveness in attracting students, teachers and financing. Unmistakably, higher education institutions are able to attain these goals only via constant improvement of study quality. Nevertheless, the swelling scale of competition at the country’s and international levels in the higher education sector highlights the image of a higher education institution as another considerable factor.This article presents a specified concept of the image of an organization. This concept is specified while considering the peculiarities of higher education institutions; based on it, the conception of a higher education institution’s image is characterized, its main typologies are distinguished, a structural model and hierarchical levels are presented. The theoretical premises are supported by a student survey of the Faculty of Economics of Vilnius University, one of the most prestigious higher education institutions in Lithuania.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Influence of the characteristics of competences on the employability of graduates of Higher Education Institutions

2020 ◽  
Vol 3 (3) ◽  
Author(s):  
Miguel Cueva Zavala
Keyword(s):  
Higher Education ◽  
Labor Market ◽  
Public Sector ◽  
Human Resources ◽  
Human Resource ◽  
Higher Education Institutions ◽  
Education Institution ◽  
Institutions Of Higher Education ◽  
Contemporary World ◽  
Active Population

This research has a singular and notable importance, because if something should concern a Higher Education Institution, it is knowing what is the destiny within society of the human resource trained in its classrooms, that product that the institution delivers to the community who are its graduates and professionals. For the Institutions of Higher Education it is satisfactory on the part of employers, that the training received in the Institution of Higher Education is indicated, that the majority of graduates and professionals are incorporated into the occupational market; that is to say; some exercise their profession and others do it in occupations that do not correspond to their profession, which is justified, being aware that one of the great problems of the contemporary world is undoubtedly the lack of demand for human resources for stable work, which according to Authorized and reliable studies of every 10 people who join the economically active population, only 3 have real possibilities of fully joining the labor market, either in the private or public sector.

DEMOCRATIZATION OF THE SOCIAL ENVIRONMENT OF HIGHER EDUCATION INSTITUTION AS A PREREQUISITE FOR THE INDIVIDUALS’ DEVELOPMENT POTENTIAL OF THE SUBJECTS OF ITS ACTION

2020 ◽  
pp. 7-24
Author(s):  
Volodymyr Ryabchenko
Keyword(s):  
Higher Education ◽  
Human Capital ◽  
Social Environment ◽  
Higher Education Institutions ◽  
Social Systems ◽  
Education Institution ◽  
Human Capital Development ◽  
The Social ◽  
Conceptual Problems ◽  
Capital Development

There are following prerequisites outlined in this article: worldwide democratization trend; complexity of structures of social systems; growing needs in human capital development; autonomy of national higher education institutions; civilizational problem of Ukraine in national elite. Conceptual problems on a road to real democracy in higher education institutions were actualized and analyzed. Determined and characterized three models of higher education institutions activities based on the level of democratization needs of their social environment as: negative, neutral and favorable.

Sign in / Sign up

Export Citation Format

Share Document