Application Of Fuzzy Neural Network Research Articles

Application and Empirical Analysis of Fuzzy Neural Networks in Mining Social Media Users’ Behavioral Characteristics and Formulating Accurate Online Marketing Strategies

In the current digital social environment, social media platforms have become an important position for user behavior insights and precision marketing. User behavioral data on social media contain rich information, but they are often fuzzy, uncertain and highly complex. Fuzzy neural network (FNN), as an advanced model combining fuzzy logic and neural network theory, provides a powerful tool for processing and analyzing social media user behavioral features. This study is dedicated to exploring the application of fuzzy neural networks in social media user behavior analysis and their key role in the design of accurate online marketing strategies. We construct and optimize a fuzzy neural network model by meticulously classifying and quantifying user behavioral features, including behavioral frequency features, content topic features, social interaction features, and time series features, as well as applying fuzzy set theory to deal with fuzzy features such as emotional states. Through empirical analysis, we will show how fuzzy neural networks can reveal the intrinsic laws behind user behaviors, and how these insights can be used to design and implement precise online marketing strategies to improve advertising effectiveness, user engagement, and brand loyalty.

Application of Fuzzy Neural Network for Sensory Evaluation of Fresh Milk

Intelligent computer programs that can estimate and predict future states hold significant potential as 'software sensors' for determining the properties of foods. Fuzzy neural network (FNN) has demonstrated its value in handling ambiguous and incomplete information, as well as integrating human expertise into process models. This study explores the application of this advanced artificial intelligence tool in the sensory evaluation of fresh milk, a daily consumed product.

Application of Fuzzy Neural Network in Teaching Spoken English for Tourism

Application of Fuzzy Neural Network in Teaching Spoken English for Tourism

Intelligent Collaborative Application of Fuzzy Neural Network and CAD in Green Architectural Design

Intelligent Collaborative Application of Fuzzy Neural Network and CAD in Green Architectural Design

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

SIMULATION OF FUZZY NEURAL NETWORK ALGORITHM IN DYNAMIC NONLINEAR SYSTEM

The identification of nonlinear system is studied to establish an accurate model of coordination system. The original dynamic fuzzy neural network (DFNN) is first used to identify the nonlinear system for finding the existing problems. Aiming at the problems found, two improvements are made. For the problem of too many pre-set parameters in the original algorithm, the fuzzy completeness [Formula: see text] is introduced to allocate the parameters, and the width of the membership function is modified. The simulation results reveal that the fuzzy neural network (FNN) model not improved produces seven fuzzy rules, and the root mean square error (RMSE) of training is 0.0261, while the improved FNN model produces six fuzzy rules, and the RMSE of training is 0.0161. The improved network has more advantages in performance. The model proposed provides some references for the application of FNN in dynamic nonlinear systems.

Feasibility of Simultaneous Application of Fuzzy Neural Network and TOPSIS Integrated Method in Potential Mapping of Lead and Zinc Mineralization in Isfahan-Khomein Metallogeny Zone

Iran is located on a silver, lead, and zinc belt and according to the latest studies holds 11 million tons of lead, zinc, and silver stones which constitute 4 percent of global resources. Considering that mineral materials are explored in an uncertain space, exploration investment risk is an inseparable part of these activities. The important fact is to minimize the effect of this undesired factor in exploration. To achieve this, it is required that exploration activities and withdrawals are performed in a certain framework in which risk minimization is considered. Using mineral potential modelling for determining promising zones which should be taken into consideration in more detailed stages could make achieving the purpose possibly. This work is aimed at applying fuzzy neural network and TOPSIS methods simultaneously in order to explore zinc and lead resources. In this article, geological, telemetry, geophysics, and geochemistry data is integrated using fuzzy-neural network (neuro fuzzy) and using TOPSIS method rating for lead and zinc ore deposit potential mapping in Isfahan-Khomein strip which has been introduced as one of zinc and leads mineral scopes in Iran. This area which is composed of several zinc and lead ore deposits has been considered as the target area. Fuzzy integration results of zinc and lead mineralization witness layers confirm the relatively high potential of lead and zinc mineralization in this region having a northwest-southeast trend and involving more than 90 percent of the known indices and ore deposits of the region. In this research, it was shown that the results of TOPSIS-Neuro-Fuzzy integrated model (a combination of neural network and fuzzy logic) have increased the resolution of talented areas from the areas with no mineralization potential in comparison with the fuzzy method individually.

Application of Fuzzy Neural Network in Recognition of Athlete's Physical State

Application of Fuzzy Neural Network in Recognition of Athlete's Physical State

Fault diagnosis of sensors in the control system of a steam turbine

A diagnostic and control system for a turbine is presented. The influence of the turbine controller on regulation processes in the power system is described. Measured quantities have been characterized and methods for detecting errors have been determined. The paper presents the application of fuzzy neural networks (fuzzy-NNs) for diagnosing sensor faults in the control systems of a steam turbine. The structure of the fuzzy-NN model and the model’s method of learning, based on measurement data, are presented. Fuzzy-NNs are used to detect faults procedures. The fuzzy-NN models are created and verified.

Application of fuzzy neural network for estimating the intensity of insulation aging of cable lines

The article considers the issues of forecasting the state of insulation of cable lines. It is shown that the insulation state is influenced by a large number of factors, some of which are indeterminate and can not be expressed in the quantitative form. It was proposed to use a fuzzy neural network for predicting the state of insulation, which ensures processing both quantitative and qualitative information, which makes it possible to obtain more valid results, in comparison with the use of only quantitative information.

Medical Images Classification and Diagnostics Using Fuzzy Neural Networks

The problem of medical images of cervix epithelium classification for express diagnostics is considered…The following states of cervix epithelium are to be recognized and classified: normal state - columnar epithelium; squamous epithelium (normal state); metaplasia-benign changes of cervix uterus epithelium; CIN1-displasia of light degree, CIN 2-displasia of middle degree, CIN 3-displasia of high degree- intra-epithelium cancer: For its solution the application of fuzzy neural network (FNN NEFClass M) is suggested. The application of FNN is grounded by its following properties: it may work with fuzzy and qualitative information; it has accelerated convergence as compared with crisp classification methods; it enables to attain better classification accuracy than conventional classifiers. The structure of FNN NEFClass and its model description are presented. Training algorithm stochastic gradient descent for membership functions of fuzzy sets is considered and implemented. Data set of medical images of cervix epithelium which was obtained by special device colposcope is described and some images are presented. The experimental investigations of FNN NEFClass application for medical images recognition on real data are carried out, the results are presented. The comparison with NN Back Propagation, RBF NN and cascade RBF NN was made and estimation of efficiency of the suggested approach was performed. The problem of reduction of features number in classification tasks using principal component method (PCM) method is considered and implemented.

Multi-targets Priority Ordering Based on Fuzzy Neural Networks

Application of fuzzy neural networks in multi-targets priority ordering is introduced in this paper. Based on the establishing of multi-targets ordering model, the multi-targets priority ordering sets, the structure and parameters for fuzzy neural networks are given in the course of multi-targets priority ordering. It has made very pleased result through experiment of certain type multi-targets priority ordering, and it is important to helicopters detective sensors management. The application of fuzzy neural networks has a good reference to multi-targets priority ordering in helicopters detective sensors management.

Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns.

Application of Fuzzy Neural Network in Predicting Small Hydropower Short-term Generating Capacity

This paper proposes a prediction method on short-term generating capacity of small hydropower projects based on fuzzy neural network, taking into consideration of the implications of factors such as season, climate, rainfall, temperature, and dates influencing small hydropower generation in a short-time. The research subject is regional small hydropower project groups as a collective. It screens out sectional flow of large hydropower projects as a correlation factor. We then conduct analysis on the large and small hydropower projects and test for the significant difference. Based on the regression models for these two, we utilize the predicted value of large hydropower for prediction of the small hydropower’s capacity, thus providing an effective solution to resolve the difficulty in capacity prediction of small hydropower in short term. The application results prove that the method produces satisfactory prediction results and it provides valuable reference to the predictability of short-term small hydropower generating capacity.

Assessment of recycling costs in product development stage

Assessment of recycling costs in the product development phase has a significant impact on the management of the product at the end of its lifetime. Recycling, which provides product management at end of life, has a significant contribution to environmental protection. Assessment of the recycling costs alternatives for certain preliminary and conceptual design in the product development phase, including the ability to choose different materials for individual parts of the product forms is the basis for managing the recycling costs. This paper presents an application of fuzzy neural network for assessment recycling costs in the product development phase for a single row cylindrical roller bearings.

Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor

ABSTRACTThis paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control system for the prediction of effluent quality and biogas production from an internal circulation (IC) anaerobic reactor system. To improve FNN performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, and a total of 5 rules were extracted in the IF-THEN format. Findings of this study clearly indicated that, compared to NN models, FNN models had smaller RMSE and MAPE as well as bigger R for the testing datasets than NN models. The proposed FNN model produced smaller deviations and exhibited a superior predictive performance on forecasting of both effluent quality and biogas (methane) production rates with satisfactory determination coefficients greater than 0.90. From the results, it was concluded that FNN modeling could be applied in IC anaerobic reactor for predicting the biodegradation and biogas production using paper-mill wastewater.

Estimation of production costs in product development phase using fuzzy-neural networks

Estimation of production costs in the product development phase, has a significant impact on achieving and maintaining the competitiveness of products on the market and the target profit, as the primary goal of production. Evaluation of individual variants conceptual and preliminary structural solutions and new product selection of a solution that has the least cost of production, the drive is controlled target costs, and target profit. This paper presents an application of fuzzy-neural network to estimate the costs of production in the product development phase to the case of single row cylindrical roller bearings.

Application of fuzzy neural networks for defining crystal lattice types in nanoscale images

The article proposes the application of neural fuzzy networks for defining the overlapping classes of crystal lattices. We discuss the following neural fuzzy networks: Takagi-Sugeno-Kung network and a modification of Wang-Mendel neural fuzzy network proposed by the authors. A three-step scheme of neural network training is proposed. The results prove the efficiency of the proposed approach for the determination of crystal lattice types.

Application of Fuzzy Neural Network in the Intelligent Control of Biomass Gasifier

In view of biomass gasify process which has nonlinear, non-minimum- phase, big delay and strong load interference characteristics, a fuzzy neural network control algorithm for biomass gasifier is presented. Gasified process of biomass gasifier has been researched, and intelligent control of temperature and primary air flow for biomass gasifier has been designed. The control objects are gasifier temperature and flue gas oxygen content , the regulation objects are the material feed flow and primary air flow. Simulation results show that, this system has better control effect than the traditional fuzzy control system.

The Application of Fuzzy Neural Network Generalized Predictive Control in Unit Plant Coordinated Control System

The characteristic of unit coordinated control in thermal power plants having complex, nonlinear, larger time delay, and establishing mathematical models are very difficult. In the paper establish mathematical models of using fuzzy neural network system, make full use of the ability of fuzzy logic reasoning and neural network self-learning; using multivariable generalized predictive control strategy, Simulation results show that the use of fuzzy neural network generalized predictive control for good stability of main steam pressure , strong effectiveness of tracking the power grid load, and little fluctuation of different load conversion.