A Greedy Incremental Algorithm for Universal Approximation with RBF Newtorks

This paper addresses mathematical aspects of fuzzy logic. The main results obtained in this paper are: 1. the introduction of a concept of normal form in fuzzy logic using hedges; 2. using Kolmogorov’s theorem, we prove that all logical operations in fuzzy logic have normal forms; 3. for min-max operators, we obtain an approximation result similar to the universal approximation property of neural networks.

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An Incremental Algorithm to Check Satisfiability for Bounded Model Checking

Electronic Notes in Theoretical Computer Science ◽

10.1016/j.entcs.2004.06.062 ◽

2005 ◽

Vol 119 (2) ◽

pp. 51-65 ◽

Cited By ~ 12

Author(s):

HoonSang Jin ◽

Fabio Somenzi

Keyword(s):

Model Checking ◽

Bounded Model Checking ◽

Incremental Algorithm

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Betweenness centrality updation and community detection in streaming graphs using incremental algorithm

Proceedings of the 6th International Conference on Software and Computer Applications - ICSCA '17 ◽

10.1145/3056662.3056673 ◽

2017 ◽

Cited By ~ 1

Author(s):

Akshita Bhandari ◽

Ashutosh Gupta ◽

Debasis Das

Keyword(s):

Community Detection ◽

Betweenness Centrality ◽

Incremental Algorithm

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Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

IEEE Transactions on Neural Networks ◽

10.1109/72.392253 ◽

1995 ◽

Vol 6 (4) ◽

pp. 911-917 ◽

Cited By ~ 235

Author(s):

Tianping Chen ◽

Hong Chen

Keyword(s):

Neural Networks ◽

Dynamical Systems ◽

Universal Approximation ◽

Activation Functions ◽

Nonlinear Operators

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Wavelet Support Vector Machine With Universal Approximation and its Application

2006 IEEE Information Theory Workshop - ITW '06 Chengdu ◽

10.1109/itw2.2006.323821 ◽

2006 ◽

Author(s):

Wenhui Chen ◽

Wanzhao Cui ◽

Changchun Zhu

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Universal Approximation

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Universal Approximation Using Radial-Basis-Function Networks

Neural Computation ◽

10.1162/neco.1991.3.2.246 ◽

1991 ◽

Vol 3 (2) ◽

pp. 246-257 ◽

Cited By ~ 2306

Author(s):

J. Park ◽

I. W. Sandberg

Keyword(s):

Finite Number ◽

Radial Basis Function ◽

Basis Function ◽

Universal Approximation ◽

Radial Basis Function Networks ◽

Feedforward Networks ◽

Rbf Networks ◽

Smoothing Factor ◽

Radial Basis ◽

Hidden Layer

There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

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