Experimental and theoretical study of the conduction mechanism and dielectric behavior of quaternary defect chalcopyrite CdInGaSe4 using adaptive neuro-fuzzy inference system (ANFIS) model

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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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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Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

Applied Sciences ◽

10.3390/app9040780 ◽

2019 ◽

Vol 9 (4) ◽

pp. 780 ◽

Cited By ~ 27

Author(s):

Khalid Elbaz ◽

Shui-Long Shen ◽

Annan Zhou ◽

Da-Jun Yuan ◽

Ye-Shuang Xu

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Pressure Balance ◽

Anfis Model ◽

Inference System ◽

Advance Rate ◽

Multi Objective ◽

Neuro Fuzzy ◽

Epb Shield

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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