Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

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.

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Prediction and Detection of User Emotions Based on Neuro-Fuzzy Neural Networks in Social Networks

Advances in Intelligent Systems and Computing - Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18) ◽

10.1007/978-3-030-01821-4_13 ◽

2018 ◽

pp. 118-125 ◽

Cited By ~ 1

Author(s):

Giovanni Pilato ◽

Sergey A. Yarushev ◽

Alexey N. Averkin

Keyword(s):

Neural Networks ◽

Social Networks ◽

Fuzzy Neural Networks ◽

Neuro Fuzzy ◽

Fuzzy Neural

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Neuro-Fuzzy Systems Approaches

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.1999.p0177 ◽

1999 ◽

Vol 3 (3) ◽

pp. 177-185 ◽

Cited By ~ 2

Author(s):

Danuta Rutkowska ◽

◽

Yoichi Hayashi ◽

Keyword(s):

Neural Networks ◽

Fuzzy Systems ◽

Transfer Functions ◽

Fuzzy Inference ◽

Back Propagation ◽

Fuzzy Neural Networks ◽

Nonparametric Approach ◽

Neuro Fuzzy ◽

Fuzzy Neural ◽

Fuzzy Weights

Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.

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Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

Electrical Engineering & Electromechanics ◽

10.20998/2074-272x.2021.1.02 ◽

2021 ◽

pp. 10-14

Author(s):

V.V. Vasilevskij ◽

M.O. Poliakov

Keyword(s):

Neural Networks ◽

Moisture Content ◽

Continuous Monitoring ◽

Fuzzy Neural Networks ◽

Transformer Oil ◽

Power Transformers ◽

Subtractive Clustering ◽

Grid Partition ◽

Neuro Fuzzy ◽

Fuzzy Neural

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.

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A direct adaptive control scheme for robot trajectory tracking using fuzzy neural networks

Iraqi Journal for Electrical And Electronic Engineering ◽

10.33762/eeej.2007.55265 ◽

2007 ◽

Vol 3 (1) ◽

pp. 1-10

Author(s):

M. Al dossari ◽

T.Y. Abdalla

Keyword(s):

Neural Networks ◽

Adaptive Control ◽

Trajectory Tracking ◽

Fuzzy Neural Networks ◽

Direct Adaptive Control ◽

Robot Trajectory ◽

Control Scheme ◽

Fuzzy Neural ◽

Adaptive Control Scheme

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Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

Archives of Metallurgy and Materials ◽

10.2478/amm-2013-0090 ◽

2013 ◽

Vol 58 (3) ◽

pp. 871-875

Author(s):

A. Herberg

Keyword(s):

Neural Networks ◽

Control System ◽

Network Structure ◽

Fuzzy Systems ◽

Fuzzy Neural Networks ◽

Cnc Machining ◽

Machining Process ◽

Modeling Process ◽

Fuzzy Neural ◽

Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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