The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Marcos Bacis Ceddia - One of the best experts on this subject based on the ideXlab platform.
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A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at -30 kPa) and Permanent Wilting Point (PWP, -1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
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IJCNN - A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at −30 kPa) and Permanent Wilting Point (PWP, −1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
L.m.c. Buydens - One of the best experts on this subject based on the ideXlab platform.
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Performance of multi-layer feedforward and Radial Base Function neural networks in classification and modelling
Chemometrics and Intelligent Laboratory Systems, 1996Co-Authors: M.s. Sánchez, E.p.p.a. Derks, H. Swierenga, Luis A. Sarabia, L.m.c. BuydensAbstract:Abstract Neural networks have been used in multiple applications, but as a kind of black box for dealing with problems where there is no a priori information about the data. This means that the model is constructed Based solely upon information obtained from the data themselves. This seems to be a good property but makes it difficult to validate the models obtained. The classification properties of neural classifiers are usually described by the percentage of correctly classified objects in a test set. Since these straight methods are only Based on discrimination, no information can be obtained in a statistical way. In this paper, on a simulated data set, two different types of neural networks, MLF (multi layer feedforward) and RBF (Radial Base Function), are applied to solve a classification problem. The modelling ability, stability and reproducibility of this kind of networks are studied Based on various different networks independently trained on the same data set with a predetermined value for the sensibility and specificity. Robustness to different kinds of error is also studied by means of Monte Carlo simulations adding noise at different levels and from different theoretical distributions. Further to this, an analysis Based on principal components is carried out to study the apparently different networks obtained. The simulation studies reveal that both types of networks perform well enough to reproduce the input space. For RBF networks, due to the local approach, the study showed some properties related to sensibility and specificity which are relevant in practical problems.
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Response to "Comment on a recent sensitivity analysis of Radial Base Function and multi-layer feed-forward neural network models"
Chemometrics and Intelligent Laboratory Systems, 1996Co-Authors: E.p.p.a. Derks, M. S. Siinchez Pastor, L.m.c. BuydensAbstract:Abstract In our paper [1], the modeling capabilities of multi-layered feed-forward (MLF) and Radial Base Function (RBF) networks were investigated on simulated data and well described experimental data from chemical industry [4]. Since both networks are Based on a different concept (that is, RBF in contrast to MLF shows more local modeling behaviour) both modeling capability and robustness to input errors have been examined. The ‘robustness’ was expressed in terms of sensitivity of the network output units to random input perturbations by means of controlled pseudo-random noise. In this response paper, the comment of Faber et al., i.e., applying theoretical error propagation on artificial neural networks, and the consequences for the conclusions drawn in the original paper [1], are addressed.
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Robustness analysis of Radial Base Function and multi-layered feed-forward neural network models
Chemometrics and Intelligent Laboratory Systems, 1995Co-Authors: E.p.p.a. Derks, M. S. Siinchez Pastor, L.m.c. BuydensAbstract:In this paper, two popular types of neural network models (Radial Base Function (RBF) and multi-layered feed-forward (MLF) networks) trained by the generalized delta rule, are tested on their robustness to random errors in input space. A method is proposed to estimate the sensitivity of network outputs to the amplitude of random errors in the input space, sampled from known normal distributions. An additional parameter can be extracted to give a general indication about the bias on the network predictions. The modelling performances of MLF and RBF neural networks have been tested on a variety of simulated Function approximation problems. Since the results of the proposed validation method strongly depend on the configuration of the networks and the data used, little can be said about robustness as an intrinsic quality of the neural network model. However, given a data set where ‘pure’ errors from input and output space are specified, the method can be applied to select a neural network model which optimally approximates the nonlinear relations between objects in input and output space. The proposed method has been applied to a nonlinear modelling problem from industrial chemical practice. Since MLF and RBF networks are Based on different concepts from biological neural processes, a brief theoretical introduction is given.
G.r. N. Carvalho - One of the best experts on this subject based on the ideXlab platform.
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A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at -30 kPa) and Permanent Wilting Point (PWP, -1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
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IJCNN - A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at −30 kPa) and Permanent Wilting Point (PWP, −1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
Sri Hartini - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Kernel-Based Clustering and Support Vector Machine Algorithm in Analyzing Cerebral Infarction Dataset
Mathematical Methods and Modelling in Applied Sciences, 2020Co-Authors: Zuherman Rustam, Dea Aulia Utami, Jacub Pandelaki, Nadisa Karina Putri, Sri HartiniAbstract:Ischemic stroke is a disease that occurs due to disruption of blood circulation to the brain due to blood clots in the brain. The blockage is called cerebral infarction. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. To deal with the problem of classification of cerebral infarction data obtained from Dr. Cipto Mangunkusumo’s Hospital in Jakarta, this study proposes the use of Fuzzy C-Means Clustering (FCM), Fuzzy Possibilistic C-Means (FPCM), and Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method as a clustering method and a Support Vector Machine (SVM) method as a classification method. This method will be compared to the level of accuracy. The greatest level of accuracy is generated from the Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method with an accuracy value of 91%.
D.n. Brandão - One of the best experts on this subject based on the ideXlab platform.
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A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at -30 kPa) and Permanent Wilting Point (PWP, -1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
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IJCNN - A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: G.r. N. Carvalho, D.n. Brandão, D.b. Haddad, V.l. Do Forte, Marcos Bacis CeddiaAbstract:The purpose of this paper was to evaluate the performance of pedotransfer Functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at −30 kPa) and Permanent Wilting Point (PWP, −1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA dataBase, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.
