Problem: feed-forward neural network - the connection between

Question

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

John Michell · Accepted Answer

% 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.
 
% My idea was to train the neural network with normal data % (so, a one-class data set) and use the net to compute a sort % of anomaly score. But, if I got it right, it has no sense to % train a neural network having just one output neuron with % data belonging to just one class. Neural networks are very % variegated and represent a vast subject. I am slowly learning % them.
 No. Create an input and target sets for 2 classes.
% As regard the default settings, I have just modified the % algorithm in order to have the classic gradient descent.
 No. Always begin using as many defaults as possible. Then 
consider (one-at-a-time) replacing default settins.
% P.s. Little question: when there is no target data defined, % isn't the problem an unsupervised machine learning problem? % Theorically, I could do it (given that my data set is not a % one-class data set).
 Your best bet is to create a 2-class dataset.

Problem: feed-forward neural network - the connection between

Need a Custom Version or Complete Simulation for This Problem?

ANN training using two time series as input

Using weights from OL in CL training; how should the weight vector

How to get validation test and training errors of a neural network?

Is there a difference between incremental training using ADAPT

Why does not Matlab use the full capacity of my computer while training a neural network?

re training neural network from previous state using trainNetwork

Modeling and Simulation of an Induction Motor Using Sliding Mode Control in MATLAB

Solar Inverter with BMS in MATLAB Simulink Complete Simulation Project

Simulation and Kinematic Analysis of a 5-DOF RRRRRP Robotic Manipulator Using MATLAB

Get a Free Consultation or a Sample Assignment Review!