Using Artificial Neural Networks in Fuzzy Time Series Analysis

2014 ◽  
pp. 443-451 ◽  
Author(s):  
Cagdas Hakan Aladag
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Analysis ◽  
Fuzzy Time Series ◽  
Series Analysis ◽  
Artificial Neural

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Sameer Al-Asheh ◽  
Farouq Sabri Mjalli ◽  
Hassan E. Alfadala
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wastewater Treatment ◽  
Time Series Analysis ◽  
Quality Indicators ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Series Analysis ◽  
Plant Data ◽  
Artificial Neural

We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

A new approach based on artificial neural networks for high order multivariate fuzzy time series

2009 ◽  
Vol 36 (7) ◽  
pp. 10589-10594 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu ◽  
Vedide R. Uslu ◽  
Murat A. Basaran
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
High Order ◽  
Fuzzy Time Series ◽  
New Approach ◽  
Artificial Neural
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