Neural network and adaptive neuro‐fuzzy inference system modeling of the hot air‐drying process of orange‐fleshed sweet potato

Abstract In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

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Model Identification of 3R Palnar Robot using Neural Network and Adaptive Neuro-Fuzzy Inference System

10.1109/icirca51532.2021.9544745 ◽

2021 ◽

Author(s):

R. Subasri ◽

R. Meenakumari ◽

R. Velnath ◽

Srinivethaa Pongiannan ◽

M. Sri Sai Mani Rohit Kumar

Keyword(s):

Neural Network ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Model Identification ◽

Inference System ◽

Neuro Fuzzy

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Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/09544089211053074 ◽

2021 ◽

pp. 095440892110530

Author(s):

Morteza Nazerian ◽

Seyed Ali Razavi ◽

Ali Partovinia ◽

Elham Vatankhah ◽

Zahra Razmpour

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Bonding Strength ◽

Fuzzy Inference ◽

Molar Ratio ◽

Inference System ◽

Neuro Fuzzy ◽

Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

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Comparison between Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) on sunlight intensity prediction based on air temperature and humidity

Journal of Physics Conference Series ◽

10.1088/1742-6596/1538/1/012044 ◽

2020 ◽

Vol 1538 ◽

pp. 012044

Author(s):

D Rahmalia ◽

T Herlambang ◽

A S Kamil ◽

R A Rasyid ◽

F Yudianto ◽

...

Keyword(s):

Neural Network ◽

Air Temperature ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Intensity Prediction ◽

Neuro Fuzzy ◽

Temperature And Humidity ◽

Sunlight Intensity

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Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽

10.3390/en11081958 ◽

2018 ◽

Vol 11 (8) ◽

pp. 1958 ◽

Cited By ~ 20

Author(s):

Lilin Cheng ◽

Haixiang Zang ◽

Tao Ding ◽

Rong Sun ◽

Miaomiao Wang ◽

...

Keyword(s):

Neural Network ◽

Wind Speed ◽

Recurrent Neural Network ◽

Fuzzy Inference ◽

Ensemble Model ◽

Probabilistic Forecasting ◽

Inference System ◽

Wind Speed Forecasting ◽

Wind Power Integration ◽

Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

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