Homework _11 Spring 11 - BME 6360 Neural Engineering...

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BME 6360 Homework #11 Spring 2011 Neural Engineering Prof. Wheeler Neural Network Pattern Recognition . This exercise is a logical continuation of the pattern recognition done in HW 10. You will execute both a Matlab version of the neural net and one written for this class which you can examine and edit for your own use and understanding. The files needed are: hw11data.mat norminpt chngoutp.m nnsetup.m nntrain.m nnpredct.m nnpick.m confused.m cluster.m . The data are all in hw11data.mat : The training data are the principal component projection values (two per action potential waveform) and are in a data set called trane (should be train , but apparently train is a keyword). The test data are similar, but taken from the second half of the spike waveforms, and are in a file called test. The target (correct class) data are stored in trainid and testid , which have the same information as ident.dat . If you get stuck, intermediate values have been stored in hw11soln.mat : trainin %normalized training data testin % normalized test data trainidm % reformatted training class id data testidm % reformatted test class id data eta, beta, tol, itermax %default parameters for class neural net R, Q, S1, S2 % sizes for Matlab neural net functions disp_freq,max_epoch, err_goal, lr, momentum, err_ratio, TP % default parameters for Matlab NN fct. Between HW10 and HW11, the row/column nature of the data matrices had to be changed for compatibility with the Matlab neural network functions. In HW11, the data trane and test are 2x41 element matrices, as compared to the 82x2 element matrix pcfeat from which they are taken. (If we were to use the data from spikes.dat (82x35), the new trane and test would be 35x41 element matrices.) Hopefully there are sufficient notes to keep you from too many mistakes with the dimensioning of the data. Neural net data need to be normalized: this will be done later by a custom function named norminpt.m . It is also necessary to change the format so that, where each row of trainid (41*1) has a single integer (e.g. 3 ), a new variable matrix, trainidm (4*41) has a four element column vector of which one element is 1 (e.g. 0 0 1 0 represents class #3). The number 4 is the number of different classes possible. Backpropagation Algorithm The algorithm is standard backpropagation. x i is the ith input. y j is the jth hidden layer output. z k is the kth output. The forward propagation equations are: y j = ! w ij x i i " # $ % & ' z k = ! w jk y j j " # $ % & ' ( ! " ( ) = 1 1 + e # "
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The backward propagation (error correction) equations are: error k = ( z k ! desired
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