Nonlinear decoding The motivation for applying a deep neural network (DNN) to the AAD problem is that a non-linear decoder may provide improved performance relative to a linear decoder due to the inherent non-linear processing of acoustic signals along the auditory pathway. A DNN is a prototypi...
We demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applie...
An efficient piecewise linear approximation of a nonlinear function (PLAN) is proposed. This uses a simple digital gate design to perform a direct transformation from X to Y, where X is the input and Y is the approximated sigmoidal output. This PLAN is then used within the outputs of an ar...
论文阅读 Neural Network Modeling of Nonlinear Systems Based on Volterra Series Extension of a Linear Mod,程序员大本营,技术文章内容聚合第一站。
tidyfit is an R-package that facilitates and automates linear and nonlinear regression and classification modeling in a tidy environment. The package includes methods such as the Lasso, PLS, time-varying parameter or Bayesian model averaging regressions, and many more. The aim is threefold: Offer...
Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach Automatica (2005) R. Byers Solving the algebraic Riccati equation with the matrix sign Linear Algebra and its Applications (1987) M. Abu-Khalaf et al. Policy iterations and the Hamilton–...
A decomposition approach to nonlinear multi-area generation scheduling with tie-line constraints using expert systems C. Wang, S. M. Shahidehpour, \A Decomposition Approach to Non-Linear Multi-Area Generation Scheduling with Tie-Line Constraints Using Ex- pert Systems... Wang,C.,Shahidehpour,....
We applied neural networks (NN) to approximate the nonlinear projection from original input space into low-dimensional space found by Isomap, followed by the sensitivity analysis to choose the best subset of predictive variables in the input space. The subset of variables with the highest rank/...
2e). When comparing the most efficient linear and nonlinear models, we find that linear models take at least one order of magnitude less time to fit than nonlinear models, as expected. However, linear methods can also be extremely complex to learn; linear models with states at the neural ...
Singing more, singing harsher: occurrence of nonlinear phenomena in a primate’ song Walter Cristiano Teresa Raimondi Cristina Giacoma Animal Cognition (2023) Effect of pitch range on dogs’ response to conspecific vs. heterospecific distress cries Holly Root-Gutteridge Victoria F. Ratcliffe David...