Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

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Universal Approximation Using Radial-Basis-Function Networks

Neural Computation ◽

10.1162/neco.1991.3.2.246 ◽

1991 ◽

Vol 3 (2) ◽

pp. 246-257 ◽

Cited By ~ 2306

Author(s):

J. Park ◽

I. W. Sandberg

Keyword(s):

Finite Number ◽

Radial Basis Function ◽

Basis Function ◽

Universal Approximation ◽

Radial Basis Function Networks ◽

Feedforward Networks ◽

Rbf Networks ◽

Smoothing Factor ◽

Radial Basis ◽

Hidden Layer

There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

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Classification Using Radial Basis Function Networks with Uncertain Weights

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.293-294.135 ◽

2005 ◽

Vol 293-294 ◽

pp. 135-142

Author(s):

Graeme Manson ◽

Gareth Pierce ◽

Keith Worden ◽

Daley Chetwynd

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Network Performance ◽

Threshold Value ◽

Radial Basis Function Networks ◽

Training Techniques ◽

Network Training ◽

Training Approach ◽

Radial Basis

This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an “unable to classify” label, are presented. The first of these considers the imposition of a threshold value on the classifier outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is dependent upon the training approach

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Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

IEEE Access ◽

10.1109/access.2020.3007337 ◽

2020 ◽

Vol 8 ◽

pp. 123087-123097

Author(s):

Mohammadreza Amirian ◽

Friedhelm Schwenker

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Convolutional Neural Networks ◽

Basis Function ◽

Radial Basis Function Networks ◽

Distance Metric ◽

Radial Basis ◽

Similarity Distance

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Identification of radial basis function networks by using revised GMDH-type neural networks with a feedback loop

Proceedings of the 41st SICE Annual Conference. SICE 2002. ◽

10.1109/sice.2002.1195514 ◽

2003 ◽

Cited By ~ 4

Author(s):

T. Kondo

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Feedback Loop ◽

Radial Basis Function Networks ◽

Radial Basis

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Comparison between traditional neural networks and radial basis function networks

2011 IEEE International Symposium on Industrial Electronics ◽

10.1109/isie.2011.5984328 ◽

2011 ◽

Cited By ~ 43

Author(s):

Tiantian Xie ◽

Hao Yu ◽

Bogdan Wilamowski

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Networks ◽

Radial Basis

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Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum

Food Control ◽

10.1016/j.foodcont.2010.05.013 ◽

2011 ◽

Vol 22 (1) ◽

pp. 88-95 ◽

Cited By ~ 22

Author(s):

Fernando Mateo ◽

Rafael Gadea ◽

Eva M. Mateo ◽

Misericordia Jiménez

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Multilayer Perceptron ◽

Fusarium Culmorum ◽

Radial Basis Function Networks ◽

Radial Basis

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An Improved Radial Basis Function Networks in Networks Weights Adjustment for Training Real-World Nonlinear Datasets

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i1.pp63-76 ◽

2019 ◽

Vol 8 (1) ◽

pp. 63

Author(s):

Lim Eng Aik ◽

Tan Wei Hong ◽

Ahmad Kadri Junoh

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Real World ◽

Basis Function ◽

Gradient Descent ◽

Radial Basis Function Networks ◽

Training Algorithm ◽

Descent Algorithm ◽

Gradient Descent Algorithm ◽

Radial Basis

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.

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