Deep Learning Toolbox™ 包含许多样本数据集，您可以使用这些数据集来试验浅层神经网络。

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.
vinyl_dataset - Vinyl bromide 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.
ovarian_dataset - Ovarian cancer dataset.
thyroid_dataset - Thyroid function dataset.
wine_dataset - Italian wines dataset.
digitTrain4DArrayData - Synthetic handwritten digit dataset for
training in form of 4-D array.
digitTrainCellArrayData - Synthetic handwritten digit dataset for
training in form of cell array.
digitTest4DArrayData - Synthetic handwritten digit dataset for
testing in form of 4-D array.
digitTestCellArrayData - Synthetic handwritten digit dataset for
testing in form of cell array.
digitSmallCellArrayData - Subset of the synthetic handwritten digit
dataset for training in form of cell array.

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, Dynamic modeling
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 modeling,
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 - Global 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

所有数据集的文件名均为 name_dataset 格式。这些文件中将包含数组 nameInputs 和 nameTargets。