scholarly journals Prediction of LAM Coke Price Using ANN and ANFIS Model

1970 ◽  
Vol 2 (3) ◽  
pp. 7-17
Author(s):  
Sanjita Jaipuria
Keyword(s):  
Fuzzy Inference ◽  
Raw Materials ◽  
Foreign Market ◽  
Raw Material ◽  
Future Price ◽  
Discrete Wavelet ◽  
Anfis Model ◽  
Inference System ◽  
The Past ◽  
Manufacturing Company

In the procurement decision, the future price of the raw material plays an important role as it affects the budget and procurement plan. Inaccuracy in raw material price prediction affects the performance of procurement activity and budget plan. Low Ash Metallurgical (LAM) coke is one of the important raw materials used by various alloy manufacturing company. In India, this coke generally imported from the foreign market and followed a long-term contract with the supplier. The LAM coke price is highly volatile in nature. Hence, accurate forecasting of the LAM coke price is very important for any alloy manufacturing company to manage its budget for the procurement. The intelligent models such as Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) can learn the past pattern through their self-learning and adapting capability. In this study, the discrete wavelet transformation has been used to extract the past LAM coke price pattern and resultant series applied to train the ANN and ANFIS model to improve the prediction accuracy. From the study, it has found that the prediction error for both the model is less than 5%, hence those models can be used by the alloy industry to predict the LAM coke price.

scholarly journals Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10 ◽  
Author(s):  
Abdullah H. Alenezy ◽  
Mohd Tahir Ismail ◽  
S. Al Wadi ◽  
Muhammad Tahir ◽  
Nawaf N. Hamadneh ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Least Square ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Stock Market Volatility ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.

scholarly journals Fuzzy Inference System pada Produksi Kerupuk Mie Bulat

2019 ◽  
Vol 1 ◽  
pp. 235
Author(s):  
Windah Sahara ◽  
Ega Widya Sari ◽  
Putri Mai Sarah Tarigan
Keyword(s):  
Material Processing ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Raw Materials ◽  
Raw Material ◽  
Uncertain Demand ◽  
Weekly Basis ◽  
Daily Lives ◽  
Inference System ◽  
Public Consumption

Raw material processing business is a business that manages raw materials into finished or semi-finished materials. At present, the effort to defeat the raw material that is in great demand by people is the business of processing raw materials that produce food products such as bread, tempeh, tofu, noodles, crackers which have become public consumption in general. For example, crackers that have a variety of flavors and shapes. One of them is round noodle crackers that we have met in many daily lives and are often consumed by the community. CV. Hariarado is one of the producers of round noodle crackers. Where Cv. Hariarado has not been able to determine production on a weekly basis due to uncertain demand. So this study aims to predict the amount of production produced by CV. Hariarado on a weekly basis. This study uses three variables, namely demand (X), inventory (Y), and production (Z). The results of the calculation show that the prediction of the amount of production produced by Cv. Hariarado with the demand of 950 ball and the supply of 1250 balls in a week is 1039 ball.

scholarly journals Bio-Based Alternatives to Phenol and Formaldehyde for the Production of Resins

Polymers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2237 ◽  
Author(s):  
P. R. Sarika ◽  
Paul Nancarrow ◽  
Abdulrahman Khansaheb ◽  
Taleb Ibrahim
Keyword(s):  
Raw Materials ◽  
Phenol Formaldehyde ◽  
Raw Material ◽  
Wood Industry ◽  
Suitable Material ◽  
The Past ◽  
Wide Range ◽  
Recent Developments ◽  
Sustainable Materials ◽  
Materials Used

Phenol–formaldehyde (PF) resin continues to dominate the resin industry more than 100 years after its first synthesis. Its versatile properties such as thermal stability, chemical resistance, fire resistance, and dimensional stability make it a suitable material for a wide range of applications. PF resins have been used in the wood industry as adhesives, in paints and coatings, and in the aerospace, construction, and building industries as composites and foams. Currently, petroleum is the key source of raw materials used in manufacturing PF resin. However, increasing environmental pollution and fossil fuel depletion have driven industries to seek sustainable alternatives to petroleum based raw materials. Over the past decade, researchers have replaced phenol and formaldehyde with sustainable materials such as lignin, tannin, cardanol, hydroxymethylfurfural, and glyoxal to produce bio-based PF resin. Several synthesis modifications are currently under investigation towards improving the properties of bio-based phenolic resin. This review discusses recent developments in the synthesis of PF resins, particularly those created from sustainable raw material substitutes, and modifications applied to the synthetic route in order to improve the mechanical properties.

The Research of Error Compensation for the 3D Surface Measurement

2012 ◽  
Vol 482-484 ◽  
pp. 2192-2196
Author(s):  
Yuan Tian ◽  
Zi Ma ◽  
Peng Li
Keyword(s):  
Measurement Error ◽  
Fuzzy Inference System ◽  
Error Compensation ◽  
Point Cloud ◽  
Fuzzy Inference ◽  
Surface Measurement ◽  
Anfis Model ◽  
Inference System ◽  
Surface Error ◽  
3D Surface

For improving precision of 3D surface measurement equipments, which are playing important role in reverse engineering, the Adaptive Network based Fuzzy Inference System (ANFIS) is developed to reconstruct 3D surface error, and the measurement error of point cloud is compensated by the presented 3D error ANFIS model. The precision of 3D surface measurement equipments has been improved noticeably

scholarly journals Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

scholarly journals Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2771 ◽  
Author(s):  
Abbas Mardani ◽  
Dalia Streimikiene ◽  
Mehrbakhsh Nilashi ◽  
Daniel Arias Aranda ◽  
Nanthakumar Loganathan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Fuzzy Inference System ◽  
Global Economy ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

scholarly journals IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Anika Nowshin Mowrin ◽  
Md. Hadiuzzaman ◽  
Saurav Barua ◽  
Md. Mizanur Rahman
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Confusion Matrix ◽  
Model Development ◽  
Model Performance ◽  
Total Sample ◽  
Dhaka City ◽  
Anfis Model ◽  
Regular User ◽  
Inference System

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train. 

scholarly journals Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System

2010 ◽  
Vol 42 (1) ◽  
pp. 159-167
Author(s):  
Masumeh Sabet ◽  
Mehdi Naseri ◽  
Hosein Sabet
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Linear Process ◽  
Back Propagation Algorithm ◽  
Littoral Drift ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Sand Drift

Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confirming its consistency with a database of specified physical process.

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

2020 ◽  
Vol 49 (4) ◽  
pp. 354-373
Author(s):  
Semih Kale
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sea Surface ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

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