A Hybrid Estimation Technique Using Elliptical Radial Basis Neural Networks and Cokriging

Samson, Matthew; Deutsch, Clayton V.

doi:10.1007/s11004-021-09969-3

Cited by 8 publications

(1 citation statement)

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“…RBF NN is a kind of forward network with good performance, there are three layers: input layer, HL with nonlinear activation function and linear output layer (OL) [5,6]. According to TNO (HL) units, it can be divided into two models, namely normalized network and generalized network [7,8]. The HL unit of the network corresponds to an activation function, and the training sample is its center point.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Person Recognition Optimization Method Relying on Radial Basis Function Network

2021

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Artificial NN is an intelligent model for information processing by simulating the organization and mechanism of the nervous system of the brain. Thanks to its strong self-learning ability, artificial NN can liberate people's labor force to a large extent, so it has been widely studied and alied. The theoretical study of artificial NN can effectively explain the alication principle and potential problems of NN, so that it can be effectively improved and developed. Therefore, the main purpose of this paper is to study the optimization method of person recognition (PR) based on RBF network. In this paper, the damping coefficients of RBF networks with uniform center distribution are studied. This stochastic learning method is an important sulement to how to determine the center and limiting factor of the RBF of the NN, and can also improve the training efficiency of the network. The main research results of this paper are developed by analyzing the convergence of stochastic RBFNN and constructing corresponding models and algorithms. Compared with other algorithms, the results show that the ARBFNN algorithm can make the RBFNN obtain a smaller training error, improve the generalization ability of the network, and have the ability to deal with large data sets and fast convergence. Recognition is more convenient and quicker.

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Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Person Recognition Optimization Method Relying on Radial Basis Function Network

2021

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Comparing and Detecting Stationarity and Dataset Shift

Silva

Nisenson

Boisvert

2023

Springer Proceedings in Earth and Environmental Sciences

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Machine learning algorithms have been increasingly applied to spatial numerical modeling. However, it is important to understand when such methods will underperform. Machine learning algorithms are impacted by dataset shift; when modeling domains of interest present non-stationarities there is no guarantee that the trained models are effective in unsampled areas. This work aims to compare the stationarity requirement of geostatistical methods to the concept of dataset shift. Also, workflow is developed to detect dataset shift in spatial data prior to modeling, this involves applying a discriminative classifier and a two sample Kolmogorv-Smirnov test to model areas. And, when required a lazy learning modification of support vector regression is proposed to account for dataset shift. The benefits of the lazy learning algorithm are demonstrated on the well-known non-stationary Walker Lake dataset and improves root mean squared error up to 25% relative to standard SVR approach, in areas where dataset shift is present.

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