A survey on hyper basis function neural networks

Zhou, Yuguo; Mu, Tong; Pang, Zhong-Hua; Zheng, Chang-Bing

doi:10.1080/21642583.2019.1699474

Cited by 7 publications

(1 citation statement)

References 53 publications

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“…RBFs are a type of neural network design that is commonly used for function approximation. (Yuguo et al, 2019). The input layer, hidden layer, and output layer make up an RBF network, which is a type of feed forward neural network with three layers.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Comparison of two different artificial neural network models for prediction of soil penetration resistance

Ünal,

Kabas,

Sözer

2023

J Agric Eng

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A time-varying, nonlinear soil-plant system contains many unknown elements that can be quantified based on analytical methodologies. Artificial Neural Networks (ANNs) are a widely used mathematical computing, modelling, and predicting method that estimates unknown values of variables from known values of others. This paper aims to simulate relationship between soil moisture, bulk density, porosity ratio, depth, and penetration resistance and to estimate soil penetration resistance with the help of ANNs. For this aim, the Generalized Regression Neural network (GRNN) and Radial Basis Function (RBF) models were developed and compared for the estimation of soil penetration resistance values in MATLAB. A dataset of 153 samples was collected from experimental field. From the 153 data, 102 data (33%) were selected for training and the remaining 51 data (67%) were used for testing. The estimation process was implemented 10 replications using randomly selected testing and training data. Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were used to evaluate estimation accuracy on the developed ANN methods. Based on MSE, RMSE, MAE and Standard Deviation (SD), statistical results showed that the GRNN modelling presented better results than the RBF model in predicting soil penetration resistance success.

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Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Comparison of two different artificial neural network models for prediction of soil penetration resistance

Ünal,

Kabas,

Sözer

2023

J Agric Eng

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State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

Ansari

Taqvi

2023

ChemBioEng Reviews

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Petrochemical industries are facing severe challenges in controlling nonlinear processes which are to be analyzed and monitored to develop efficient control models for improved efficiency and productivity. This review focuses on the control theory of petrochemical processes along with linear and nonlinear controllers with their limitations as well as on the application of artificial intelligence (AI)‐based controllers in multiple sectors of petrochemical industries. The findings and results from the review prove that the AI control technique is a promising one in the future for uncertain dynamic systems. The paper also covers a review of the vinyl chloride monomer (VCM) process and a recent control study on the plant. Being a nonlinear system, VCM requires advanced control techniques to control the uncertain dynamics of the process. The study shows a gap in the development of control strategies applied to VCM in terms of sustainability, efficiency, and environment‐concerned issues. Valuable insights for researchers, practitioners, and engineers engaged in process control and automation in the petrochemical industry are provided.

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Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Han

Cao

Geng

et al. 2023

Science of The Total Environment

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A survey on hyper basis function neural networks

Cited by 7 publications

References 53 publications

Comparison of two different artificial neural network models for prediction of soil penetration resistance

Comparison of two different artificial neural network models for prediction of soil penetration resistance

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

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