trainDetector

Train signal anomaly detector

Since R2023a

Syntax

trainDetector(d,data)

trainDetector(d,data,op)

Description

trainDetector(d,data) trains the detector object d on the training data set data.

trainDetector(d,data,op) specifies training options for the detector.

Examples

Train Convolutional Signal Anomaly Detector

Load the file sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals.

sineWaveNormal contains 10 sinusoids of stable frequency and amplitude. Each signal has a series of small-amplitude impact-like imperfections. The signals have different lengths and initial phases. Plot the first three normal signals.

load sineWaveAnomalyData
sl = 3;

tiledlayout("vertical")
ax = zeros(sl,1);
for kj = 1:sl
    ax(kj) = nexttile;
    plot(sineWaveNormal{kj})
    title("Normal Signal")
end

sineWaveAbnormal contains three signals, all of the same length. Each signal in the set has an anomaly.

• All channels of the first signal have an abrupt change in frequency that lasts for a finite time.

• The second signal has a finite-duration amplitude change in one of its channels.

• The third signal has spikes at random times in all channels.

Plot the three signals with anomalies.

for kj = 1:sl
    plot(ax(kj),sineWaveAbnormal{kj})
    title(ax(kj),"Signal with Anomalies")
end

Create a convolutional autoencoder object to detect the anomalies in the abnormal signals. Specify that the signals used for both training and testing all have three channels.

D = deepSignalAnomalyDetector(sl);

Use the trainDetector function to train the convolutional neural network using the anomaly-free data.

• Use the training options for the adaptive moment estimation (Adam) optimizer. For more information, see trainingOptions (Deep Learning Toolbox).

• Use the CPU to train the network.

• Specify a maximum number of 100 epochs to use for training.

• Use a mini-batch size of 64.

• Display the training progress as a plot.

opts = trainingOptions("adam",ExecutionEnvironment="cpu", ...
    MaxEpochs=100,MiniBatchSize=64, ...
    Verbose=false,Plots="training-progress");

trainDetector(D,sineWaveNormal,opts)

Input Arguments

d — Anomaly detector

deepSignalAnomalyDetectorCNN object | deepSignalAnomalyDetectorLSTM object | deepSignalAnomalyDetectorLSTMForecaster object

Anomaly detector, specified as a deepSignalAnomalyDetectorCNN object, a deepSignalAnomalyDetectorLSTM object, or a deepSignalAnomalyDetectorLSTMForecaster object. Use the deepSignalAnomalyDetector function to create d.

data — Training data set

N_c-column matrix | M-element cell array | timetable | datastore

Training data set, specified as one of these:

• N_c-column matrix — A single multichannel signal observation (M = 1), where N_c is equal to the value of the NumChannels property of the detector.

• M-element cell array — M multichannel signal observations, where each cell contains an N_c-column matrix.

• Timetable — A single multichannel signal observation, contained in a MATLAB^® timetable. The timetable must contain increasing, uniformly-sampled, and finite values. The timetable can have:

  • A single variable containing an N_c-column matrix, where each column corresponds to a signal channel.

  • N_c variables, where each variable contains a vector that corresponds to a signal channel.

• Datastore — A signalDatastore, audioDatastore (Audio Toolbox), or arrayDatastore object. The detector uses the readall function to read all the signal observations contained in the datastore at once. You can also use a CombinedDatastore or TransformedDatastore object containing any of the supported datastores.

op — Training options

TrainingOptionsADAM object (default) | TrainingOptionsSGDM object | TrainingOptionsRMSProp object

Training options, specified as a TrainingOptionsADAM, TrainingOptionsSGDM, or TrainingOptionsRMSProp object. Use trainingOptions (Deep Learning Toolbox) to create the object.

• If the ModelType property of the detector is specified as "convautoencoder" or "lstmforecaster", the training options default to a TrainingOptionsADAM (Deep Learning Toolbox) object.

• If the ModelType property of the detector is specified as "lstmautoencoder", the training options default to a TrainingOptionsADAM (Deep Learning Toolbox) object with SequenceLength equal to 128.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

The ExecutionEnvironment option must be "gpu" or "auto" when the input data is:

• A gpuArray

• A cell array containing gpuArray objects

• A datastore that outputs cell arrays containing gpuArray objects

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2023a