RBFNRadial Basis Function Network
RBFNRadial Basis Function Neural Networks
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Proposed System Classification of cells by networks of Gaussian radial basis functions (RBFN) and morphological descriptors.
In the first experiment, RBFN consisted of 16 neurons, whose centers were randomly distributed in a square area bounded by points (-0.2; -0.2) and (1.2; 1.2).
From the functional perspective, ANFIS architecture is, on the one hand, an equivalent of fuzzy model as defined by Takagi-Sugeno-Kang (TSK model) and, on the other hand, a rough equivalent of Radial Basis Function Networks (RBFNs).
The performance comparison of the proposed system with three commonly used classifiers in the literature (MLP, RBFN, and SVM) with similar complexity indicates that the proposed system has no compromise on detection accuracy.
In the current study, a two-step approach is proposed to identify the quasi-ARX RBFN model for the nonlinear systems.
An RBFN is an example of a multilayer feedforward network as it contains precisely one hidden layer [5].
We consider the following examples to illustrate theoretical results of Theorems 1 and 3 and to compare numerical results of the method with the results of HPM and RBFN method.
In [26], by combining the RBFN and the neuron model with the IIR filter, a network was proposed and it was used to predict time series.
have proved through comparison of various classification techniques like support vector machine (SVM) with polynomial kernel, support vector machine with RBF kernel, radial basis function network (RBFN), and multilayer perceptron network (MLP) with and without feature extraction.
Gomi and Kawato proposed a feedback error learning control strategy, where a Gaussian RBFN is used for online learning of the inverse dynamics of the system [6].
Then a radial basis function network (RBFN) is trained on the residuals of the bagging predictions on the training dataset and is used to provide the estimate [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
Choi, "An adaptive neurocontroller using RBFN for robot manipulators," IEEE Transactions on Industrial Electronics, vol.51, no.3, pp.711-717, 2004.