scholarly journals FORECASTING GOLD PRICE CHANGES BY USING ADAPTIVE NETWORK FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 13 (5) ◽  
pp. 994-1010 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini ◽  
Siamak Haji Yakhchali ◽  
Diana Volungevičienė ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Gold Price ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Price Changes ◽  
Inference System

Developing a precise and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination (R 2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms of different performance criteria during the training and validation phases. Sensitivity analysis showed that the gold price changes are highly dependent upon the values of silver price and oil price.

scholarly journals Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

2020 ◽  
Vol 11 (1) ◽  
pp. 203
Author(s):  
Primož Jelušič ◽  
Andrej Ivanič ◽  
Samo Lubej
Keyword(s):  
Fuzzy Inference System ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Ground Vibration ◽  
Residential Building ◽  
Ground Vibrations ◽  
Adaptive Network ◽  
Inference System ◽  
Fast Learning

Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

scholarly journals Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1444
Author(s):  
Saeed Na’amnh ◽  
Muath Bani Salim ◽  
István Husti ◽  
Miklós Daróczi
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classical Method ◽  
Fuzzy Model ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.

scholarly journals PERANCANGAN MODEL PENDUKUNG KEPUTUSAN PENGADAAN BARANG DENGAN MENGGUNAKAN FUZZY INFERENCE SYSTEM

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Viera Astry ◽  
Dadang Surjasa ◽  
Dedy Sugiarto
Keyword(s):  
Fuzzy Inference System ◽  
Consumer Satisfaction ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Arima Model ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Validation Process ◽  
Inference System ◽  
Verification Process

<p>Alleriea is a small medium enterprises engaged in the field of providing souvenirs. To increase consumer satisfaction, the company should be able to fullfill consumer demand, The decisions support system in this study is using Fuzzy Inference System with Mamdani type as intuitive and very suitable to be given expert knowledge. This model was designed using MATLAB software and as input will be used to predict the number of requests, the speed of supply and stock condition.<br />The predicted number of demand are made by using forecasting methods by selecting a forecasting model with the smallest MSE value. Based on the comparison of the value of MSE on the ARIMA model and winter, forecasting results obtained by the method of Winter has the smallest MSE value.<br />The verification process is done by looking at the forecasting model with the smallest MSE, the validation process is done to test the normality of residual data. The verification process on fuzzy inference systems is done by testing whether the rules given leave in accordance with the desired output. The validation process using a combination of testing Extreme Test uses a combination of extreme in any condition. The result of this paper is a procurement decision support model using fuzzy inference system which influenced by the demand forecast, stock condition and speed of supply. Designed models have been verified and validated.</p>

Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

2019 ◽  
Vol 234 (3) ◽  
pp. 438-451 ◽  
Author(s):  
Mostafa Jalal ◽  
Poura Arabali ◽  
Zachary Grasley ◽  
Jeffrey W Bullard
Keyword(s):  
Compressive Strength ◽  
Relative Humidity ◽  
Silica Fume ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Criteria ◽  
System Model ◽  
Rubberized Concrete ◽  
Inference System ◽  
Neuro Fuzzy

Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.

scholarly journals Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Lu Minh Le ◽  
Hai-Bang Ly ◽  
Binh Thai Pham ◽  
Vuong Minh Le ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Buckling Load ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Steel Columns ◽  
Neuro Fuzzy ◽  
Hybrid Artificial Intelligence

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

Multilayer Optimization Approach for Fuzzy Systems

2011 ◽  
pp. 1121-1129
Author(s):  
Ivan N. Silva ◽  
Rogerio A. Flauzino
Keyword(s):  
Energy Function ◽  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Energy Functions ◽  
Inference System ◽  
Squared Error ◽  
The Mean ◽  
Inference Systems

The design of fuzzy inference systems comes along with several decisions taken by the designers since is necessary to determine, in a coherent way, the number of membership functions for the inputs and outputs, and also the specification of the fuzzy rules set of the system, besides defining the strategies of rules aggregation and defuzzification of output sets. The need to develop systematic procedures to assist the designers has been wide because the trial and error technique is the unique often available (Figueiredo & Gomide, 1997). In general terms, for applications involving system identification and fuzzy modeling, it is convenient to use energy functions that express the error between the desired results and those provided by the fuzzy system. An example is the use of the mean squared error or normalized mean squared error as energy functions. In the context of systems identification, besides the mean squared error, data regularization indicators can be added to the energy function in order to improve the system response in presence of noises (from training data) (Guillaume, 2001). In the absence of a tuning set, such as happens in parameters adjustment of a process controller, the energy function can be defined by functions that consider the desired requirements of a particular design (Wan, Hirasawa, Hu & Murata, 2001), i.e., maximum overshoot signal, setting time, rise time, undamped natural frequency, etc. From this point of view, this article presents a new methodology based on error backpropagation for the adjustment of fuzzy inference systems, which can be then designed as a three layers model. Each one of these layers represents the tasks performed by the fuzzy inference system such as fuzzification, fuzzy rules inference and defuzzification. The adjustment procedure proposed in this article is performed through the adaptation of its free parameters, from each one of these layers, in order to minimize the energy function previously specified. In principle, the adjustment can be made layer by layer separately. The operational differences associated with each layer, where the parameters adjustment of a layer does not influence the performance of other, allow single adjustment of each layer. Thus, the routine of fuzzy inference system tuning acquires a larger flexibility when compared to the training process used in artificial neural networks. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, such methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno.

scholarly journals Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

2020 ◽  
Vol 17 (10) ◽  
pp. 3520 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Laith Abualigah ◽  
Mohamed Abd Elaziz
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inference System ◽  
Infected People ◽  
Marine Predators ◽  
Forecasting Performance ◽  
The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

scholarly journals Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Fuzzy Inference System ◽  
Metal Foam ◽  
Fuzzy Inference ◽  
Cfd Modeling ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Inlet Temperature ◽  
Inference System ◽  
Turbulence Parameters

Abstract The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.

scholarly journals The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Abdullahi Garba Usman ◽  
Mubarak Hussaini Ahmad ◽  
Rabi’u Nuhu Danraka ◽  
Sani Isah Abba
Keyword(s):  
Linear Model ◽  
Fuzzy Inference System ◽  
Molecular Mechanisms ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Inflammatory Biomarkers ◽  
Herbal Products ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Comparison Of The Results

Abstract Background Medicinal plants are used to manage pain and inflammatory disorders in traditional medicine. A scientific investigation could serve as a basis for the determination of molecular mechanisms of antinociceptive and antiinflammatory actions of herbal products. In this work, we used both artificial intelligence (AI) based models inform of adaptive neuro-fuzzy inference system and artificial neural network (ANN) as well as a linear model, namely; stepwise linear regression in modelling the performance of four different inflammatory biomarkers namely; interleukin (1L)-1β, 1L-6, tumour necrosis factor (TNF)-α and prostaglandin E2 (PGE2). This modelling was done using number of abdominal writes, the reaction time of paw licking in mice and paw oedema diameter as the input variables. Results Four different performance indices were employed, which are determination coefficient (DC), root mean squared error (RMSE), mean square error (MSE) and correlation co-efficient (CC). The results have shown the superiority of the AI-based models over the linear model. Conclusions The overall quantitative and visualized comparison of the results showed that adaptive neuro-fuzzy inference system outperformed the ANN and SWLR models in modelling the performance of the four inflammation biomarkers in both the calibration and verification phases.

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