Neuro-fuzzy Approach Research Articles

Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

People tend to build and maintain their friendship relying on SNS nowadays. Thus, the problem of how to organize the social network accurately and automatically. In this paper, a hybrid neuro-fuzzy approach is used. . Many aspect impact the error values like input/output, membership functions, the training data arrays, and the number of epochs needed to train the model. This paper is based on hybrid Neuro-Fuzzy concept for testing the link prediction for facebook data. We use Matlab to calculate average testing Error, View Generation Rule, Output Surface.

Intelligence Decision Making of Fault Detection and Fault Tolerances Method for Industrial Robotic Manipulators

FD methods are usually based on the residual generation and analysis concept. A mathematical model is used to reproduce the dynamic behavior of the fault-free system; the deviation of the output predicted by the model from actual output measurements forms the so-called residuals. Which, when properly analyzed, provides valuable information about failure. Based on the failure an intelligent decision is taken with the help of the neuro fuzzy fault diagnosis system. The main aim of this work is the introduction of a new algorithm for robots fault detection which forms part of a proposed intelligent decision making framework for fault tolerance in robotic manipulator. In developing the model, this work explores the affects of failures in an example robot using a technique called Neuro-Fuzzy Approach. The robot components critical to fault detection are revealed using a Neuro-Fuzzy (NF) approach. To evaluate our NF based fault detection and tolerance method we performed an extensive simulation study with a Scorbot ER 5u plus robot manipulator. In this research work we considered all faults possible to occur in robot manipulator. The Scorbot ER 5u plus model was developing in robotics toolbox for MATLAB using the NF algorithms

Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real-world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally.

Enhancing interpretation of uncertain information in navigational commands for service robots using neuro-fuzzy approach

Enhancing interpretation of uncertain information in navigational commands for service robots using neuro-fuzzy approach

Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.

Bitcoin price forecasting with neuro-fuzzy techniques

Cryptocurrencies, with Bitcoin being the most notable example, have attracted considerable attention in recent years, and they have experienced large fluctuations in their price. While a few studies employ conventional statistical and econometric approaches to reveal the driving factors of Bitcoin's prices, research on the development of forecasting models to be used as decision support tools in investment strategies is scarce. This study proposes a computational intelligence technique that uses a hybrid Neuro-Fuzzy controller, namely PATSOS, to forecast the direction in the change of the daily price of Bitcoin. The proposed methodology outperforms two other computational intelligence models, the first being developed with a simpler neuro-fuzzy approach, and the second being developed with artificial neural networks. Furthermore, the investment returns achieved by a trading simulation, based on the signals of the proposed model, are 71.21% higher than the ones achieved through a naive buy-and-hold strategy. The performance of the PATSOS system is robust to the use of other cryptocurrencies.

Adaptive neuro fuzzy inference system approach for developing soil water retention pedotransfer functions in flood spreading areas

When using indirect approaches for accurate estimation of soil hydraulic properties, it is important to consider time and cost savings as part of water management. Models based on an adaptive neuro fuzzy inference system (ANFIS) outperform artificial neural networks (ANNs) in terms of forecasting error, computational speed, and estimation. The present study used ANFIS to develop pedotransfer functions (PTFs) to estimate soil hydraulic parameters (van Genuchten water retention curve parameters α, n, and residual water content [θr]), soil water retention at saturation, field capacity (FC), and permanent wilting point (PWP) using basic soil properties such as soil particle size distribution, bulk density (BD), medium porosity (0.2 to 30 μm [7.9 × 10−6 to 0.001 in]), and organic carbon (C). The ANN-based Rosetta model and ANFIS-based PTF were compared for accuracy of prediction of saturated soil water content (θs), FC, and PWP of soil in the flood spreading areas of Iran. The ANFIS-based models were able to estimate soil hydraulic properties with reasonable accuracy. It was concluded that adding medium porosity (0.2 to 30 μm) as an input variable to the ANFIS-based models improved model accuracy for FC, α, and n. Except for the SSCBDθ33 model, prediction of water content at FC and PWP by Rosetta improved significantly over those obtained using the ANFIS-based PTFs.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Auto-Scaling Approach for Cloud based Mobile Learning Applications

In the last decade, mobile learning applications have attracted a significant amount of attention. Huge investments have been made to develop educational applications that can be implemented on mobile devices. However, mobile learning applications have some limitations, such as storage space and battery life. Cloud computing provides a new idea to solve some limitations of mobile learning applications. However, there are other limitations, like scalability, that must be solved before mobile cloud learning can become completely operational. There are two main problems with scalability. The first occurs when the application server’s performance declines due to an increase in the number of requests, which affects usability. The second is that a decrease in the number of requests makes most application servers idle and therefore wastes money. These two problems can be avoided or minimized by provisioning auto-scaling techniques that permit the acquisition and release of resources dynamically to accommodate demand. In this paper, we propose an intelligent neuro-fuzzy reinforcement learning approach to solve the scalability problem in mobile cloud learning applications, and evaluate the proposed approach against some of the existing approaches via MATLAB. The large state space and long training time required to find the optimal policy are the main problems of reinforcement learning. We use fuzzy Q-learning to solve the large state space problem by grouping similar variables in the same state; there is then no need to use large look-up tables. The use of parallel learning agents reduces the training time needed to determine optimal policies. The experimental results prove that the proposed approach is able to increase learning speed and reduce the training time needed to determine optimal policies.

Analiza površine požara u šumi primenom neuro fazu tehnike

Analiza površine požara u šumi primenom neuro fazu tehnike

A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

<p><span>In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of </span><span lang="EN-MY">monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination </span><span lang="EN-SG">(R<sup>2</sup>). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R<sup>2</sup>) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.</span></p>

Heat Transfer and Fluid Flow Optimization of Titanium Dioxide–Water Nanofluids in a Turbulent Flow Regime

In this study, the convection heat transfer and pressure drop of titanium dioxide–water nanofluids were modeled by applying the fuzzy C-means adaptive neuro-fuzzy inference system approach for a completely developed turbulent flow based on experimentally obtained training and test datasets. Two models were proposed based on the effective parameters; one model was developed for the Nusselt number considering the effects of the Reynolds number, Prandtl number, nanofluid volume concentration and average nanoparticle diameter. Another model was suggested for the pressure drop of the nanofluid as a function of the Reynolds number, nanofluid volume concentration, and average nanoparticle diameter. The results of these two proposed models were compared with experimental data as well as the existing correlations in the literature. The validity of the proposed models was benchmarked by statistical criteria. Moreover, a modified non-dominated sorting genetic algorithm multiobjective optimization technique was applied to obtain the optimum design points, and the final result was shown in a Pareto front.

Modified Bird swarm algorithm for edge detection in noisy images using fuzzy reasoning

ABSTRACTA new bio-inspired edge detection approach is proposed to deal with the noisy images using a combination of bird swarm algorithm (BSA) and fuzzy reasoning. BSA is based on the behaviour of the birds. The birds fly through each pixel while they forage for the food and detect the edge pixels and noisy pixels that fall in their path. The direction in which the birds fly is found using fuzzy rule-based system. The pixels are classified as edge and non-edge pixels using the concept of thresholding. The noisy pixels are removed by the birds using fuzzy impulse noise detection and reduction method. The technique has been evaluated on two standard image datasets for quantitative and qualitative analysis. The results clearly indicate significant improvement over the other bio-inspired approaches, namely, Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Bacterial Foraging Algorithm (BFO), Deep Learning approach, fuzzy with BFO for noisy images, Neuro-fuzzy approach and PSO particularly for images having impulse noise in terms of Entropy, Kappa Value, Pratt’s Figure of Merit (FoM) and Structural Similarity Index Measure (SSIM). The proposed approach works well to detect the continuous, thin and smooth edges in the presence of 5–40% noise density.

Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant

The present work proposes a new solar power station surveillance approach based on the photovoltaic module failures diagnosis using an adaptive neuro-fuzzy inference approach. Indeed, the main aim of this proposed approach is to ensure and increased energy efficiency and improved reliability of the studied solar power station. This approach is used to generate faults indicators, to detect, locate and isolate the faults based on modeling of the main characteristic variables based on an adaptive neuro-fuzzy inference, where the main aim is the prediction of the expected studied system behavior based on the actual collected measurements of the studied system. Where, the investigation field of this work is implanted on an area of 60 ha it contains 120120 solar panels with an efficiency of 15–20% with a total power of 30 MW connected to the electrical network of 30 KV. The obtained results confirm the validity of the proposed approach in improving the reliability and the overall efficiency of the studied power system. It was proved experimentally that after a sandstorm, the normal operating mode thresholds were exceeded and absolute overshoots of 35.5, 5.6 and 1.3 were registered for the output power, the output voltage and the output current respectively. These registrations have permitted to identify the failures and to set up a decision for the cleaning of the photovoltaic module. Indeed, It has been proved in this work that the operation state mode can be maintained based on failure detection and its maintenance which can be achieved in time thanks to the proposed approach.

Phishing-Aware: A Neuro-Fuzzy Approach for Anti-Phishing on Fog Networks

Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection. In this paper, we use uniform resource locator features and Web traffic features to detect phishing websites based on a designed neuro-fuzzy framework (dubbed Fi-NFN). Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks. The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.

Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Industrial site selection involves a large number of criteria and location alternatives; consequently, the selection process leads to extended decision-making periods and requires complex knowledge management, classification, and analysis skills. The selection criteria are generally described by a number of different features expressed as both quantitative and qualitative measures that can involve some uncertainty. Moreover, the goals considered in the selection process are frequently nonlinearly related to the criteria; thus, they give rise to an optimization problem that is nonlinear with respect to each goal. Consequently, decision making requires appropriate support to enable efficient data optimization and classification under uncertainty before the final selection of an industrial site is made. This paper presents a novel intelligent decision support system for classifying industrial sites according to quality criteria estimated by exploiting a geographic information system, expert knowledge, and machine learning techniques. The proposed system is based on a geographic information system for generating location alternatives and a hierarchical neuro-fuzzy approach for site classification. The neuro-fuzzy method is based on a knowledge base designed by experts in the field and uses a neural approach to tune the parameters of the membership functions. Experimental results on real-world problems show that the proposed system provides accurate results for industrial site classification at the local level (micro locations).

Drought modeling: a comparative study between time series and neuro-fuzzy approaches

Meteorological drought is one of the inseparable climatic phenomena in sub-tropical countries such as Iran. In these areas, which encompass the vastest deserts of the world, the effects of precipitation scarcity on water resources manifest themselves promptly. This study employed the standardized precipitation index (SPI), as a meteorological drought assessment tool, over 3- and 12-month time scales during the years 1970 to 2014. We compared the accuracy of the neuro-fuzzy model (as a non-linear model) with time-series models for modeling of drought. Time-series analysis was conducted according to the Box–Jenkins method. ARIMA (3, 0, 4) and ARIMA (2, 0, 1) were selected as the best-fitting time-series models for modeling SPI at time scales of 3 and 12 months, respectively. The results indicated that the neuro-fuzzy model significantly outperforms the time-series models. The Nash–Sutcliffe efficiency (NSE) coefficients are equal to 0.12 and 0.60 respectively for SPI3 and SPI12 estimated by ARIMA model, while NSE coefficients for neuro-fuzzy model are equal to 0.52 and 0.80 respectively for SPI3 and SPI12 in validation period. Also, the violin plots demonstrated that the neuro-fuzzy model (unlike the ARIMA model) is well-suited to estimate the volatility of SPI values for wet and dry periods, which is a very important prerequisite for efficient water resources’ management.

NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT

ABSTRACT Urban afforestation has important functions, but problems related to its management are equally relevant, analysis of which is needed in order to prevent accidents. However, due to the subjectivity in the assessment, there may be uncertainty as to the seriousness of the risk. In order to address this, the present work evaluates a neuro-fuzzy-based methodology for the integrated analysis of risk indicators. From the knowledge of experts and a database with 107 cases, systems were constructed for the multi-criteria analysis of 18 parameters integrated using 3 indexes and 5 indicators. As a result, the model presented accuracies of 95.5% in generalization tests, and almost perfect agreement (kappa > 0.8) with the assessment by the expert. In conclusion, the findings show that this neuro-fuzzy modeling approach represents a promising alternative for supporting risk analysis in urban afforestation.