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Neural Network Toolbox Sample Data Sets

The Neural Network Toolbox™ software contains a number of sample data sets that you can use to experiment with the functionality of the toolbox. To view the data sets that are available, use the following command:

help nndatasets
  Neural Network Datasets
  Function Fitting, Function approximation and Curve fitting.
  Function fitting is the process of training a neural network on a
  set of inputs in order to produce an associated set of target
  outputs. Once the neural network has fit the data, it forms a
  generalization of the input-output relationship and can be used 
  to generate outputs for inputs it was not trained on.

   simplefit_dataset     - Simple fitting dataset.
   abalone_dataset       - Abalone shell rings dataset.
   bodyfat_dataset       - Body fat percentage dataset.
   building_dataset      - Building energy dataset.
   chemical_dataset      - Chemical sensor dataset.
   cho_dataset           - Cholesterol dataset.
   engine_dataset        - Engine behavior dataset.
   house_dataset         - House value dataset
  Pattern Recognition and Classification
  Pattern recognition is the process of training a neural network
  to assign the correct target classes to a set of input patterns.
  Once trained the network can be used to classify patterns it has
  not seen before.
   simpleclass_dataset   - Simple pattern recognition dataset.
   cancer_dataset        - Breast cancer dataset.
   crab_dataset          - Crab gender dataset.
   glass_dataset         - Glass chemical dataset.
   iris_dataset          - Iris flower dataset.
   thyroid_dataset       - Thyroid function dataset.
   wine_dataset          - Italian wines dataset.
  Clustering, Feature extraction and Data dimension reduction
  Clustering is the process of training a neural network on
  patterns so that the network comes up with its own
  classifications according to pattern similarity and relative
  topology.  This is useful for gaining insight into data, or 
  simplifying it before further processing.
   simplecluster_dataset - Simple clustering dataset.
  The inputs of fitting or pattern recognition datasets may also
  Input-Output Time-Series Prediction, Forecasting, Dynamic
  modelling, Nonlinear autoregression, System identification
  and Filtering
  Input-output time series problems consist of predicting the 
  next value of one time-series given another time-series.
  Past values of both series (for best accuracy), or only one
  of the series (for a simpler system) may be used to predict the
  target series.
   simpleseries_dataset  - Simple time-series prediction dataset.
   simplenarx_dataset    - Simple time-series prediction dataset.
   exchanger_dataset     - Heat exchanger dataset.
   maglev_dataset        - Magnetic levitation dataset.
   ph_dataset            - Solution PH dataset.
   pollution_dataset     - Pollution mortality dataset.
   valve_dataset         - Valve fluid flow dataset.
  Single Time-Series Prediction, Forecasting, Dynamic modelling,
  Nonlinear autoregression, System identification, and Filtering
  Single time-series prediction involves predicting the next value
  of a time-series given its past values.
   simplenar_dataset     - Simple single series prediction dataset.
   chickenpox_dataset    - Monthly chickenpox instances dataset.
   ice_dataset           - Gobal ice volume dataset.
   laser_dataset         - Chaotic far-infrared laser dataset.
   oil_dataset           - Monthly oil price dataset.
   river_dataset         - River flow dataset.
   solar_dataset         - Sunspot activity dataset

Notice that all of the data sets have file names of the form name_dataset. Inside these files will be the arrays nameInputs and nameTargets. You can load a data set into the workspace with a command such as

load simplefit_dataset

This will load simplefitInputs and simplefitTargets into the workspace. If you want to load the input and target arrays into different names, you can use a command such as

[x,t] = simplefit_dataset;

This will load the inputs and targets into the arrays x and t. You can get a description of a data set with a command such as

help maglev_dataset
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