matlab 2012 神经网络数据集

 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.
  vinyl_dataset         - Vinyl bromine dataset.


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 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.
  ovarian_dataset       - Ovarian cancer dataset.
  thyroid_dataset       - Thyroid function dataset.
  wine_dataset          - Italian wines dataset.


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 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 clustered.


 ----------


 Input-Output Time-Series Prediction, Forecasting, Dyanamic 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.
  refmodel_dataset      - Reference model dataset
  robotarm_dataset      - Robot arm 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

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