Results 201 to 210 of about 66,291 (256)

QuKAN: A Quantum Circuit Born Machine Approach to Quantum Kolmogorov Arnold Networks. [PDF]

Sci Rep
Werner Y, Malemath A, Liu M, Fortes Rey V, Palaiodimopoulos N, Lukowicz P, Kiefer-Emmanouilidis M.   +6 more
europepmc   +1 more source

Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures. [PDF]

Sci Rep
Alqarni M, Alqarni A.
europepmc   +1 more source

Higher-Order Triadic Interactions: Insights Into the Multiscale Network Organization in Schizophrenia. [PDF]

Hum Brain Mapp
Li Q, Yu S, Malo J, Pearlson GD, Wang YP, Calhoun VD.   +5 more
europepmc   +1 more source

Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization. [PDF]

Sci Rep
Wei R, Chen H, Wang H.
europepmc   +1 more source

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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.
George A Anastassiou
exaly   +4 more sources

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
Doron Stein, Arie Feuer
openaire   +2 more sources

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
openaire   +2 more sources

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 ...
openaire   +2 more sources