Use of the error gradient to minimize continuous differentiable multivariate functions provides a substantial improvement in efficiency over algorithms that do not make use of the gradient information.
Folding dynamics are ubiquitously involved in controlling the multivariate functions of RNAs.
In mathematics, a PDE is a differential equation that contains unknown multivariate functions and their partial derivatives.
In this paper we obtain sharp Ostrowski type inequalities for multidimensional sets of bounded variation and multivariate functions of bounded variation.
This paper presents a new general neural structure based on nonlinear flexible multivariate function that can be viewed in the framework of the generalised regularisation networks theory.
For example, it is preferable to extract some coherent relationships between temperature and pressure in some space when we try to extract features of differential topology from data samples of a multivariate function R 3 → R 2 in this case.
For FastIVA, it is a multivariate function of the summation of the desired signals in all frequency bins.
It has proven its versatility in numerous applications, including context-aware recommender systems and multivariate function learning.
It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer.
