scholarly journals THE EFFECTIVENESS OF THE LEARNING ALGORITHM OF RADIAL BASIS NETWORKS WITH RELATION TO THE TRANSFER FUNCTIONS APPLIED ON THE EXAMPLE OF MAPPING OF THE LIE LAND OF ZIELONA GORA CITY

2005 ◽  
Vol 1 ◽  
pp. 48
Author(s):  
Maria Mrowczynska
Keyword(s):  
Transfer Functions ◽  
Learning Algorithm ◽  
Ground Surface ◽  
Green Matrix ◽  
Fourth Degree ◽  
Radial Basis Network ◽  
Gauss Function ◽  
Radial Basis ◽  
Hardy Function ◽  
Matrix Pseudoinverse

The article presents problems connected to the use of radial basis networks for the approximation of the ground surface. The main goal of this paper is to research into the precision of topographic profile representation with relation to the transfer functions applied. The paper contains a description of the structure of a radial basis network and a description of networks learning by means of the hybrid method with the use of the notion of the Green matrix pseudoinverse. Special attention was given to the problem of a choice of transfer functions: the Gauss function, the exponential function, the Hardy function, the spliced function of the third and fourth degree as well as bicentral functions with an independent slope and rotation. the result of this article is an example of the operation of a network with relation the transfer functions under discussion.

scholarly journals Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Mary Opokua Ansong ◽  
Jun Steed Huang ◽  
Mary Ann Yeboah ◽  
Han Dun ◽  
Hongxing Yao
Keyword(s):  
Hybrid Model ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Network Efficiency ◽  
Gaussian Type ◽  
Squared Error ◽  
The Neural Network ◽  
Radial Basis ◽  
Non Gaussian ◽  
Complexity Increase

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models.

Improving the Generalization Properties of Radial Basis Function Neural Networks

1991 ◽  
Vol 3 (4) ◽  
pp. 579-588 ◽  
Author(s):  
Chris Bishop
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Error Functional ◽  
Radial Basis ◽  
The Error Functional ◽  
Network Mapping ◽  
Nonlinear Mappings

An important feature of radial basis function neural networks is the existence of a fast, linear learning algorithm in a network capable of representing complex nonlinear mappings. Satisfactory generalization in these networks requires that the network mapping be sufficiently smooth. We show that a modification to the error functional allows smoothing to be introduced explicitly without significantly affecting the speed of training. A simple example is used to demonstrate the resulting improvement in the generalization properties of the network.

Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 56-80 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Neural Networks ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Basis Functions ◽  
Second Phase ◽  
Rbf Neural Networks ◽  
Radial Basis ◽  
Two Phases

In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.

PREDICTION OF LYCOPENE STABILITY IN TOMATO PRODUCTS BY RADIAL BASIS NETWORK ANALYSIS

2013 ◽  
pp. 149-154
Author(s):  
M. Cámara ◽  
V. Fernández Ruiz ◽  
D. Fernández Redondo ◽  
J.S. Torrecilla ◽  
M.C. Sánchez Mata
Keyword(s):  
Network Analysis ◽  
Radial Basis Network ◽  
Radial Basis ◽  
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