Neural Network Toolbox™ User's Guide

1,738 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach (2016, methodology): “The implementation of the NN method is quite straightforward as easy-to-use and highly optimised NN training software packages are available [46].”
Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS (2015, methodology): “The minimum error was observed by using one hidden layer with eight neurons [50].”
A comprehensive evaluation of multicategory classification methods for microbiomic data (2013, background): “K-nearest neighbors (KNN) algorithm treats all objects as points in m-dimensional space (where m is the number of features) and given an unseen object, the algorithm classifies it by a vote of K nearest training objects as determined by some distance metric, typically Euclidian distance [15,22].”
Tree height prediction approaches for uneven-aged beech forests in northwestern Spain (2013, methodology): “All the above models were constructed using the neural network toolbox in Matlab software (Beale et al., 2012).”
Learning an Approximate Model Predictive Controller With Guarantees (2018, methodology): “Incremental learning and batch learning There are different ways to learn NN [36].”