Bee-inspired RBF network for volume estimation of individual trees

Silva, Eugenio Monteiro da; Maia, Renato Dourado; Cabacinha, Christian Dias

doi:10.1016/j.compag.2018.07.036

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…Recently, Mirjalili [33] demonstrated that the hybrid of evolutionary algorithm such as particle swarm optimization (PSO) with RBFN shows a good performance in classification roblems and approximation problems. The used of evolutionary algorithm as a tool to select more accurate centers are also reported in many recent literatures [7,[33][34][35][36][37][38] for RBFN training indeed is a good method if the networks training speed and computation cost are not main concerns.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, RBF networks turn popular among researchers. Many researchers whom have been working to produce more effective training algorithms, set alongside the standard techniques [1][2][3][4][5][6][7]. RBF networks are useful in approximation problems, but it requires a long time to teach the networks as it pertains to a huge number of training data, yet create a high error because of possible 55 invalid data or outlier in the training data.…”

Section: Introductionmentioning

confidence: 99%

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Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks

Aik

Hong

Junoh

2019

IJ-AI

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The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN 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 proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks

Aik

Hong

Junoh

2019

IJ-AI

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“…ANNs have also been widely used in forest management, including the estimation of tree height, diameter, and volume [27,32].Various types of networks have been used to meet these aims, including radial basis functions (RBF) [33] and, the most frequent ones, one-or two-layer perceptron MLP [34][35][36][37]. Conceptually, the models used in these works differed not only in architecture, but also in data input and model evaluation.…”

Section: Introductionmentioning

confidence: 99%

Stem Taper Approximation by Artificial Neural Network and a Regression Set Models

Socha

Netzel

Cywicka

2020

Forests

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Variation in tree stem form depends on species, age, site conditions, etc. Stem taper models that estimate stem diameter at any height and volume should comply with this complexity. In the paper, we propose new methods taking into account both unbiased estimates and stem variability: (i) an expert model based on an artificial neural network (ANN) and (ii) a statistical model built using a regression tree (REG). We used the variable-exponent taper equation (STE) as a reference for these two models. Input data contain information about 2856 trees representing eight dominant forest-forming tree species in Poland (birch, beech, oak, fir, larch, alder, pine, and spruce). The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built ANN and REG models and calculated both stem taper and tree volumes. The results show that ANN is a universal approach that offers the most precise estimation of stem diameter at a particular stem height for different tree species. The results for alder are an exception. In this case, the REG model performs slightly better than ANN. In terms of volume prediction, the ANN model provides the most accurate predictions for coniferous and beech. In general, flexibility and predictive performance of the ANN are better than REG and reference the STE equation.

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“…RBFN displayed its advantages over other types of neural networks with better approximation abilities, simple network design and faster learning algorithms. Each year, there are a lot of finding done on RBFN, including theoretical research, algorithm design, and numerous applications in various areas such as pattern recognition, classification, prediction, control system and image processing [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets

Aik

Hong

Junoh

2019

IJ-AI

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In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN 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 I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.

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Bee-inspired RBF network for volume estimation of individual trees

Cited by 18 publications

References 16 publications

Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks

Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks

Stem Taper Approximation by Artificial Neural Network and a Regression Set Models

An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets

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