To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer Perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.
标签： input-output the generated following
上传时间： 2014-01-05
上传用户：royzhangsz
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer Perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
标签： Rauch-Tung-Striebel algorithm smoother which
上传时间： 2016-04-15
上传用户：zhenyushaw
一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)
上传时间： 2016-11-06
上传用户：康郎
％For the following 2-class problem determine the decision boundaries ％obtained by LMS and Perceptron learning laws.
标签： boundaries the following determine
上传时间： 2016-11-26
上传用户：guanliya
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward Perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches.
标签： backpropagation implementating Lightweight lightweight
上传时间： 2013-12-27
上传用户：清风冷雨
