Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state

This work evaluated artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modelling methods to estimate organic carbon removal using the correlation among the past information of influent and effluent parameters in a full-scale aerobic biological wastewater treatment plant. Model development focused on providing an adaptive, useful, practical and alternative methodology for modelling of organic carbon removal. For both models, measured and predicted effluent COD concentrations were strongly correlated with determination coefficients over 0.96. The errors associated with the prediction of effluent COD by the ANFIS modelling appeared to be within the error range of analytical measurements. The results overall indicated that the ANFIS modelling approach may be suitable to describe the relationship between wastewater quality parameters and may have application potential for performance prediction and control of aerobic biological processes in wastewater treatment plants.

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The Research of Error Compensation for the 3D Surface Measurement

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.482-484.2192 ◽

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

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/9831252 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 9

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.

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ANFIS Model for Time Series Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1411 ◽

2013 ◽

Vol 385-386 ◽

pp. 1411-1414 ◽

Cited By ~ 4

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.

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

Energies ◽

10.3390/en11102771 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2771 ◽

Cited By ~ 12

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.

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

Oceanological and Hydrobiological Studies ◽

10.1515/ohs-2020-0031 ◽

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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Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

Water Science & Technology Water Supply ◽

10.2166/ws.2019.146 ◽

2019 ◽

Vol 20 (1) ◽

pp. 148-156

Author(s):

Seyed Hesam Alihosseini ◽

Ali Torabian ◽

Farzam Babaei Semiromi

Keyword(s):

Fuzzy Inference System ◽

Quality Index ◽

Municipal Wastewater ◽

Fuzzy Inference ◽

Treatment Plant ◽

Wastewater Effluent ◽

Inference System ◽

Effluent Quality ◽

Proposed Model ◽

Treated Municipal Wastewater

Abstract The issues of freshwater scarcity in arid and semi-arid areas could be reduced via treated municipal wastewater effluent (TMWE). Artificial intelligence methods, especially the fuzzy inference system, have proven their ability in TMWE quality evaluation in complex and uncertain systems. The primary aim of this study was to use a Mamdani fuzzy inference system to present an index for agricultural application based on the Iranian water quality index (IWQI). Since the uncertainties were disregarded in the conventional IWQI, the present study improved this procedure by using fuzzy logic and then the fuzzy effluent quality index (FEQI) was proposed as a hybrid fuzzy-based index. TMWE samples of the Gheitarie wastewater treatment plant in Tehran city recorded from 2011 to 2017 were taken into consideration for testing the ability of the proposed index. The results of the FEQI showed samples categorized as ‘Excellent’ (21), ‘Good’ (10), ‘Fair’ (4), and ‘Marginal’ (1) for the warm seasons, and for the cool seasons, the samples categorized as ‘Excellent’, ‘Good’ and ‘Fair’ were 17, 18 and 1, respectively. Generally, a comparison between the IWQI and proposed model results revealed the FEQI's superiority in TMWE quality assessment.

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