scholarly journals Application of particle swarm optimization with ANFIS model for double scroll chaotic system

2021 ◽  
Vol 11 (1) ◽  
pp. 328
Author(s):  
W. A. Wali
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Anfis Model ◽  
Inference System ◽  
Nonlinear Resistor ◽  
Additional Information ◽  
Neuro Fuzzy ◽  
Chaotic Time Series Prediction

The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.

scholarly journals A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1071 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Xiaohui Cui
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Oil Consumption ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Problems

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

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.

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
Vol 1 (1) ◽  
pp. 60-69 ◽  
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

Prediction of Electroencephalogram Time Series via Artificial Neuro-Fuzzy Inference System Trained by League Championship Algorithm

2018 ◽  
pp. 232-248
Author(s):  
Mehmet Mehdi Karakoc
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Electroencephalogram Eeg ◽  
Analyze Time Series ◽  
Over Time

Time series analysis has a wide application interest in artificial-intelligence-oriented research studies. Because it is easy to run machine-learning-based solutions directly over time series, it has been a popular approach to use alternative types of intelligent systems to analyze time series. Regarding such works, time series prediction is known as a remarkable topic as followed by researchers from different fields. The objective of this chapter is to provide an alternative work by using artificial neuro-fuzzy inference system trained by the league championship algorithm, which is an optimization algorithm from the associated literature. As the application objective, electroencephalogram (EEG) time series have been tried to be predicted by using the designed ANFIS-LCA approach. The chapter briefly introduces details about the approach and reports findings from the performed prediction operations.

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