Problem: feed-forward neural network - the connection between the hidden layer and output layer is removed.
I am facing a strange problem with Matlab and, in particular, with the training of a feed-forward neural network.
In practice, I set the network, which is formed by an input layer, a hidden layer and an output layer. But, when I call the train function, the connection between the hidden layer and the output layer is removed and I do not understand why. I hope someone can help me.
The following is the simple code I use:
if true load fisheriris feedforwardNetwork = feedforwardnet(10); feedforwardNetwork.divideFcn = 'dividetrain'; feedforwardNetwork.trainFcn = 'traingd'; feedforwardNetwork.trainParam.epochs = 10; feedforwardNetwork = train(feedforwardNetwork, meas'); end
ANSWER
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% Hi Greg and Brendan. Thanks for your reply. % % Well, after struggling reading the Matlab documentation, % I think I understood what the problem was. % % The code I posted was just a dummy example to explain the % issue I was facing. My real problem is the following: I am % trying to solve an anomaly detection problem and, in % particular, reading sensor data, I am trying to detect when % there is an anomaly behavior. % % In order to do so, I am using different machine learning % algorithms and evaluating their performance. So far, I have % used the nearest neighbor algorithm, the self-organizing maps % and the support vector machines. Another "instrument" I would % like to use is that of neural networks.
Your problem is that you did not do the following:
1. Identify the problem as one of the following
a. regression/curvefitting b. classification/patternrecognition c. clustering d. time-series 2. Search both NEWSGROUP and ANSWERS using a. classification b. pattern-recognition to identify a. classification/pattern-recognition functions (e.g., patternnet) b. example classification/pattern-recognition code and data examples 3. Practice using one or more of the MATLAB classification/... pattern-recognition example data obtained from help nndata doc nndata
5. Apply what is learned above on your dataset.
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