An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price

Abdollahi, Hooman

doi:10.4018/ijamc.2020040105

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Subsequently, an inference system is fashioned by combining a set of fuzzy if-then rules with a neural network. These fuzzy rules elucidate the relationship between the input features and target values, while the neural network adjusts parameters to align with the data . The ANFIS model can be mathematically expressed as eq Y = f ( x ) = W 0 + W 1 x 1 + · · · + W n x n where Y signifies the predicted value for the input features x , W 0 is the bias term, and W 1 , W 2 ,···, and W n are the weights assigned to each feature.…”

Section: Methodsmentioning

confidence: 99%

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Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

Pourramezan,

Rohani,

Abbaspour-Fard

2023

ACS Omega

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The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80%), attaining a RMSE between 0.09 and 0.20, a mean absolute percentage error between 0.23 and 0.43, and an EF of 0.99. This study provides valuable insights for optimizing and implementing machine learning models in predicting the viscosity of engine lubricants. Limitations include the dataset size, potentially affecting the generalizability of findings, and the omission of other factors impacting engine performance. Nevertheless, this study establishes groundwork for future research on the application of soft computing tools in engine oil analysis and condition monitoring.

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Section: Methodsmentioning

confidence: 99%

“…These fuzzy rules elucidate the relationship between the input features and target values, while the neural network adjusts parameters to align with the data. 75 The ANFIS model can be mathematically expressed as eq 2 74…”

mentioning

confidence: 99%

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

Pourramezan,

Rohani,

Abbaspour-Fard

2023

ACS Omega

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“…Reference [60] suggested a method to predict the price of oil by employing SVM. Reference [61] improved the application of ANN techniques to address the oil price forecasting problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Fathalla¹,

Alameer²,

Abbas³

et al. 2023

Computer Systems Science and Engineering

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The oil industries are an important part of a country's economy. The crude oil's price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil's price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries (OPEC) oil price forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More precisely, the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA (1,1,1), ARIMA (0,1,1), autoregressive moving average (ARMA) (0,1), vector autoregression (VAR), random walk (RW), support vector machine (SVM), and random forests (RF) models by 99.94%, 99.62%, 99.87%, 99.65%, 7.55%, 98.38%, and 99.35%, respectively, according to mean square error metric.

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“…In recent years, a hybridization of ANFIS with many optimization algorithms has been introduced to improve the forecasting accuracy of the traditional ANFIS. Abdollahi [8] introduced a novel hybrid model for forecasting the Australian option price market. In a hybrid process, it consists of an entropy method and ANFIS trained by PSO.…”

Section: Introductionmentioning

confidence: 99%

The Application of a Hybrid Model Using Mathematical Optimization and Intelligent Algorithms for Improving the Talc Pellet Manufacturing Process

Buntam¹,

Permpoonsinsup²,

Surin³

2020

Symmetry

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Moisture is one of the most important factors impacting the talc pellet process. In this study, a hybrid model (HM) based on the combination of intelligent algorithms, self-organizing map (SOM), the adaptive neuron fuzzy inference system (ANFIS) and metaheuristic optimizations, genetic algorithm (GA) and particle swarm optimization (PSO) is introduced, namely, HM-GA and HM-PSO. The main purpose is to predict the moisture in the talc pellet process related to symmetry in the aspect of real-world application problem. In the combination process, SOM classifies the suitable input data. The GA and PSO, as the training algorithms of ANFIS, are investigated to compare the prediction skill. Five factors, including talc powder, water, temperature, feed speed, and air flow of 52 experiment cases designed by central composite design (CCD), are the training set data. Three different measures evaluate the capacity of moisture prediction. The comparison results show that the HM-PSO can provide the smallest difference between train and test datasets under the condition of the moisture being less than 5%. As a result, the HM-PSO model achieves the best result in predicting the moisture for the talc pellet process with R = 0.9539, RMSE = 1.0693, and AAD = 0.393, compared to others.

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An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price

Cited by 8 publications

References 44 publications

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The Application of a Hybrid Model Using Mathematical Optimization and Intelligent Algorithms for Improving the Talc Pellet Manufacturing Process

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