Simultaneous input variable and basis function selection for RBF networks

Radial Basis Function (RBF) Neural Netwo

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems || RBF Neural Network Design and Simulation

✍ Liu, Jinkun 📂 Article 📅 2012 🏛 Springer Berlin Heidelberg 🌐 German ⚖ 607 KB

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design metho

The automatic model selection and variab

The automatic model selection and variable kernel width for RBF neural networks

✍ Peng Zhou; Dehua Li; Hong Wu; Feng Cheng 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 827 KB

Radial Basis Function (RBF) Neural Netwo

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems || Adaptive RBF Observer Design and Sliding Mode Control

✍ Liu, Jinkun 📂 Article 📅 2012 🏛 Springer Berlin Heidelberg 🌐 German ⚖ 468 KB

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design metho

Structure selective updating for nonline

Structure selective updating for nonlinear models and radial basis function neural networks

✍ W. Luo; S. A. Billings 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 242 KB 👁 2 views

Selective model structure and parameter updating algorithms are introduced for both the online estimation of NARMAX models and training of radial basis function neural networks. Techniques for on-line model modification, which depend on the vector-shift properties of regression variables in linear m

Using feedforward neural networks and fo

Using feedforward neural networks and forward selection of input variables for an ergonomics data classification problem

✍ Chuen-Lung Chen; David B. Kaber; Patrick G. Dempsey 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 829 KB

## Abstract A method was developed to accurately predict the risk of injuries in industrial jobs based on datasets not meeting the assumptions of parametric statistical tools, or being incomplete. Previous research used a backward‐elimination process for feedforward neural network (FNN) input varia

Feature selection for high-dimensional m

Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks

✍ Kui Zhang; Yuhua Li; Philip Scarf; Andrew Ball 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 568 KB