resetState

Reset model internal states

Since R2024a

Syntax

resetState(d)

Description

resetState(d) clears the memory of past data patterns and prepares the deepSignalAnomalyDetectorLSTMForecaster model d so you can use detect on a new signal.

Note

resetState does not reset the trained network.

Examples

Detect Anomalies in Streaming Sinusoids

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

• sineWaveNormal contains the 10 sinusoids used to train the convolutional anomaly detector. Each signal has a series of small-amplitude impact-like imperfections but otherwise has stable amplitude and frequency.

• sineWaveAbnormal contains three signals of similar length and amplitude to the training data. One of the signals has an abrupt, finite-time change in frequency. Another signal has a finite-duration amplitude change in one of its channels. A third has random spikes in each channel.

Plot three normal signals and the three signals with anomalies.

load sineWaveAnomalyData
 
tiledlayout(3,2,TileSpacing="compact",Padding="compact")
rnd = randperm(length(sineWaveNormal));
for kj = 1:length(sineWaveAbnormal)
    nexttile
    plot(sineWaveNormal{rnd(kj)})
    title("Normal Signal")
    nexttile
    plot(sineWaveAbnormal{kj})
    title("Signal with Anomalies")
end

Create a long short-term memory (LSTM) forecaster object to detect the anomalies in the abnormal signals. Specify a window length of 10 samples.

D = deepSignalAnomalyDetector(3,"lstmforecaster",windowLength=10);

Train the forecaster using the anomaly-free sinusoids. Use the training options for the adaptive moment estimation (Adam) optimizer and specify a maximum number of 100 epochs. For more information, see trainingOptions (Deep Learning Toolbox).

opts = trainingOptions("adam",MaxEpochs=100,ExecutionEnvironment="cpu");
trainDetector(D,sineWaveNormal,opts)

    Iteration    Epoch    TimeElapsed    LearnRate    TrainingLoss
    _________    _____    ___________    _________    ____________
            1        1       00:00:01        0.001          0.6369
           50       50       00:00:08        0.001         0.19706
          100      100       00:00:17        0.001        0.064225
Training stopped: Max epochs completed
Computing threshold...
Threshold computation completed.

Use the trained detector to find the anomalies in the first signal. Reset the state of the detector. Stream the data one sample at a time and have the detector keep its state after each reading. Compute the reconstruction loss for each one-sample frame. Categorize signal regions where the loss exceeds a specified threshold as anomalous.

resetState(D)

sg = sineWaveAbnormal{1};
anoms = NaN(size(sg));
losss = zeros(size(sg));

for kj = 1:length(sg)
    frame = sg(kj,:);
    [lb,lo] = detect(D,frame, ...
        KeepState=true,ExecutionEnvironment="cpu");
    anoms(kj) = lb;
    losss(kj) = lo;
end

Plot the anomalous signal, the reconstruction loss, and the categorical array that declares each sample of the signal as being anomalous or not.

figure
tiledlayout("vertical")
nexttile
plot(sg)
nexttile
plot(losss)
nexttile
stem(anoms,".")

Reset the state of the detector. Find the anomalies in the third signal. Plot the anomalous signal, the reconstruction loss, and the categorical array that declares each sample of the signal as being anomalous or not.

resetState(D)

sg = sineWaveAbnormal{3};
anoms = NaN(size(sg));
losss = zeros(size(sg));

for kj = 1:length(sg)
    frame = sg(kj,:);
    [lb,lo] = detect(D,frame, ...
        KeepState=true,ExecutionEnvironment="cpu");
    anoms(kj) = lb;
    losss(kj) = lo;
end

figure
tiledlayout("vertical")
nexttile
plot(sg)
nexttile
plot(losss)
nexttile
stem(anoms,".")

Input Arguments

d — Anomaly detector

deepSignalAnomalyDetectorLSTMForecaster object

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

Version History

Introduced in R2024a