Results 201 to 210 of about 65,893 (254)

Cubic approximation neural network for multivariate functions

Neural Networks, 1998
This paper introduces a novel neural network architecture-cubic approximation neural network (CANN), capable of local approximation of multivariate functions. It is particularly simple in concept and in structure. Its simplicity enables a quantitative evaluation of its approximation capabilities, namely, for a desired error bound the size of the needed
D, Stein, A, Feuer
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Multivariate sigmoidal neural network approximation

Neural Networks, 2011
Here we study the multivariate quantitative constructive approximation of real and complex valued continuous multivariate functions on a box or RN, N∈N, by the multivariate quasi-interpolation sigmoidal neural network operators. The "right" operators for our goal are fully and precisely described.
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Approximations by multivariate perturbed neural network operators

Analysis and Applications, 2017
This article deals with the determination of the rate of convergence to the unit of each of three newly introduced here multivariate perturbed normalized neural network operators of one hidden layer. These are given through the multivariate modulus of continuity of the involved multivariate function or its high-order partial derivatives and that ...
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Constructive Approximation to Multivariate Function by Decay RBF Neural Network

IEEE Transactions on Neural Networks, 2010
It is well known that single hidden layer feedforward networks with radial basis function (RBF) kernels are universal approximators when all the parameters of the networks are obtained through all kinds of algorithms. However, as observed in most neural network implementations, tuning all the parameters of the network may cause learning complicated ...
Muzhou, Hou, Xuli, Han
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Multivariate Fuzzy Perturbed Neural Network Approximations

2015
This chapter studies the determination of the rate of convergence to the unit of each of three newly introduced here multivariate fuzzy perturbed normalized neural network operators of one hidden layer. These are given through the multivariate fuzzy modulus of continuity of the involved multivariate fuzzy number valued function or its high order fuzzy ...
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Approximations by Multivariate Perturbed Neural Networks

2015
This chapter deals with the determination of the rate of convergence to the unit of each of three newly introduced here multivariate perturbed normalized neural network operators of one hidden layer.
openaire   +1 more source

Deep Neural Network-Based Algorithm Approximation via Multivariate Polynomial Regression

2019 IEEE Global Communications Conference (GLOBECOM), 2019
Many communication tasks have been formulated as optimization problems that can be solved by iterative algorithms. However, these algorithms are usually computationally intensive. To enable real-time processing of communication algorithms, in this paper, we propose a new deep neural network (DNN) architecture for algorithm approximation.
Chunmiao Liu, Bowen Shi, Chenglin Li, Junni Zou, Yingqi Chen, Hongkai Xiong   +5 more
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Univariate Sigmoidal Neural Network Quantitative Approximation

2011
Here we present the multivariate quantitative constructive approximation of real and complex valued continuous multivariate functions on a box or ℝ N , N eℕ, by the multivariate quasi-interpolation sigmoidal neural network operators. The “hright” operators for the goal are fully and precisely described.
openaire   +1 more source

Multivariate Fuzzy-Random Normalized Neural Network Approximation

2015
In this chapter we study the rate of multivariate pointwise convergence in the q-mean to the Fuzzy-Random unit operator or its perturbation of very precise multivariate normalized Fuzzy-Random neural network operators of Cardaliaguet-Euvrard and “Squashing” types.
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Multivariate Fuzzy-Random Perturbed Neural Network Approximations

2015
In this chapter we study the rate of multivariate pointwise and uniform convergences in the q-mean to the Fuzzy-Random unit operator of perturbed multivariate normalized Fuzzy-Random neural network operators of Stancu, Kantorovich and Quadrature types.
openaire   +1 more source