Research on the Non-Linear Function Fitting of RBF Neural Network

2013 ◽  
Author(s):  
Liu Jin-Yue ◽  
Zhu Bao-Ling
Keyword(s):  
Neural Network ◽  
Linear Function ◽  
Rbf Neural Network ◽  
Function Fitting ◽  
Non Linear

Fault Surface Models of Coal Rake Based on RBF Neural Network

2014 ◽  
Vol 484-485 ◽  
pp. 616-619
Author(s):  
Xiao Li Pan ◽  
Li Hua Mu ◽  
Hui Chen
Keyword(s):  
Neural Network ◽  
Linear Function ◽  
Surface Reconstruction ◽  
High Precision ◽  
Rbf Neural Network ◽  
Coal Extraction ◽  
Coal Surface ◽  
Fault Surface ◽  
Surface Models ◽  
Cloud Of Points

In order to improve the accuracy of prospecting and efficiency of coal extraction, it is necessary to understand the geological construction deeply. Therefore, the reconstruction of fault surface models is highly important. Reconstructe surface from an unorganized cloud of points by using the RBF neural networkcs advantages of approximating no-linear function, and the algorithmcs scheme and analyses were given and the proposed method was applied to the coal surface reconstruction, this neural network can not only approximate the surface with high precision but also has good smoothness.

Sensitivity Analysis of an Information Maximization Approach to the Blind Separation of the Vibration Responses of Defective Rolling Element Bearings

2005 ◽  
Author(s):  
C. Yiakopoulos ◽  
I. Antoniadis
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Linear Function ◽  
Network Structure ◽  
Vibration Response ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Non Linear ◽  
Vibration Responses ◽  
Neural Network Structure

Vibration response of rotating machines is typically mixed and corrupted by a variety of interfering sources and noise, leading to the necessity for the isolation of the useful signal components. A relevant frequently encountered industrial case is the need for the separation of the vibration responses of the same type of bearings inside the same machine. For this purpose, a Blind Source Separation procedure has been successfully applied, based on the maximization of the information transferred in a neural network structure. Thus, a key element for the success of the proposed procedure is the non-linear function used in this single layer Neural Network structure. However, since the vibration response of defective rolling element bearings is characterized by signals with super-Gaussian distributions, a sensitivity analysis of this non-linear function is necessary. First, this analysis is performed in a set of numerical experiments, based on dynamic models of defective bearings. Finally, the same analysis is applied in an experimental test rig.

scholarly journals Daily Peak Load Forecasting by Structured Representation on Genetic Algorithms for Non-linear Function Fitting

2004 ◽  
Vol 124 (3) ◽  
pp. 355-362 ◽  
Author(s):  
Kazuto Yukita ◽  
Shinya Kato ◽  
Yasuyuki Goto ◽  
Katsuhiro Ichiyanagi ◽  
Yasuhiro Kawashima
Keyword(s):  
Genetic Algorithms ◽  
Linear Function ◽  
Peak Load ◽  
Load Forecasting ◽  
Function Fitting ◽  
Non Linear ◽  
Structured Representation

Study of the Transducer Fault Diagnosis Module for Fire Alarm System

2013 ◽  
Vol 462-463 ◽  
pp. 45-50
Author(s):  
Min Lin Liu ◽  
Bo Yun Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rbf Neural Network ◽  
Alarm System ◽  
Intelligent Algorithms ◽  
Fire Alarm ◽  
Higher Dimensional ◽  
Non Linear ◽  
Fire Alarm System

As for different systems, there are much more intelligent algorithms for the sensors fault diagnosis. Some improvements and alternatives can be applied to several aspects of research. Many sensors fault modality are non-linear or general higher dimensional shapes to the diagnosis problem thus allowing to model arbitrarily complex failure phenomena. In the paper, the transducer fault diagnosis module introduces the information fusion basing on RBF neural network and the redundancy calculation, it shows that the failure of the fire alarm sensors can be detected and rehabilitated.

CLASS OF BOOLEAN FUNCTIONS CONSTRUCTED USING SIGNIFICANT BITS OF LINEAR RECURRENCES OVER THE RING ℤ2n

2019 ◽  
Vol 6 (2) ◽  
pp. 90-94
Author(s):  
Hernandez Piloto Daniel Humberto
Keyword(s):  
Linear Function ◽  
Boolean Functions ◽  
Hamming Distance ◽  
Linear Recurrences ◽  
Maximum Period ◽  
Non Linear ◽  
The Given ◽  
Class Of Functions

In this work a class of functions is studied, which are built with the help of significant bits sequences on the ring ℤ2n. This class is built with use of a function ψ: ℤ2n → ℤ2. In public literature there are works in which ψ is a linear function. Here we will use a non-linear ψ function for this set. It is known that the period of a polynomial F in the ring ℤ2n is equal to T(mod 2)2α, where α∈ , n01- . The polynomials for which it is true that T(F) = T(F mod 2), in other words α = 0, are called marked polynomials. For our class we are going to use a polynomial with a maximum period as the characteristic polyomial. In the present work we show the bounds of the given class: non-linearity, the weight of the functions, the Hamming distance between functions. The Hamming distance between these functions and functions of other known classes is also given.

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