نتایج جستجو برای: Universal Approximator

تعداد نتایج: 106435  

2004
Michael Georgiopoulos Georgios C. Anagnostopoulos Gregory L. Heileman

A measure of s k c e s s for any learning algorithm is how u s e ful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically h e applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we wi...

1999
Andrzej Lozowski Rafal Komendarczyk Jacek M. Zurada

The existence of a Hamiltonian vector field in which trajectories of the invariant set of a dissipative hyperbolic chaotic system are embedded will be proved (see notation below). Evidence of this with an example concerning the Lorenz system will be provided. Also, a constructive method of designing a Hamiltonian for the Lorenz attractor with a universal approximator will be introduced. The pre...

Journal: :iranian journal of fuzzy systems 0
dechao li school of mathematics, physics and information science, zhejiang ocean university, zhoushan, zhejiang, 316022, china and key laboratory of oceanographic big data mining and application of zhejiang province, zhoushan, zhejiang, 316022, china yongjian xie college of mathematics and information science, shaanxi normal university, xi'an, 710062, china

it is firstly proved that the multi-input-single-output (miso) fuzzy systems based on interval-valued $r$- and $s$-implications can approximate any continuous function defined on a compact set to arbitrary accuracy.  a formula to compute the lower upper bounds on the number  of interval-valued fuzzy sets needed to achieve a pre-specified approximation  accuracy for an arbitrary multivariate con...

2001
Saman K. Halgamuge

|A novel technique of designing application speci c defuzzi cation strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzi cation approximator is validated by showing the convergence when approximating several existing defuzzi cation strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. The ...

Journal: :IEEE Trans. Fuzzy Systems 1998
Saman K. Halgamuge

A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. T...

1996
Ansgar Heinrich Ludolf West David Saad Ian T. Nabney

The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even f...

In this paper, first the space of hyperbolic tangent functions is introduced and then the universal approximator property of this space is proved. In fact, by using this space, any nonlinear continuous function can be uniformly approximated with any degree of accuracy. Also, as an application, this space of functions is utilized to design feedback control for a nonlinear dynamical system.

Journal: :IEEE transactions on neural networks 1999
Erol Gelenbe Zhi-Hong Mao Yan-Da Li

This paper examines the function approximation properties of the "random neural-network model" or GNN. The output of the GNN can be computed from the firing probabilities of selected neurons. We consider a feedforward Bipolar GNN (BGNN) model which has both "positive and negative neurons" in the output layer, and prove that the BGNN is a universal function approximator. Specifically, for any f ...

Journal: :Journal of Machine Learning Research 2016
Simon Odense Roderick Edwards

The Restricted Boltzmann Machine (RBM) has proved to be a powerful tool in machine learning, both on its own and as the building block for Deep Belief Networks (multi-layer generative graphical models). The RBM and Deep Belief Network have been shown to be universal approximators for probability distributions on binary vectors. In this paper we prove several similar universal approximation resu...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید