Method and apparatus providing a trained signal classification neural network

Patent metadata

Field	Value
Publication number	US-11580382-B2
Application number	US-201916395395-A
Country	US
Kind code	B2
Filing date	Apr 26, 2019
Priority date	Apr 26, 2019
Publication date	Feb 14, 2023
Grant date	Feb 14, 2023

Abstract

A method for providing a training data set used for training a signal classification neural network is provided. The method includes generating at least one first virtual waveform primitive comprising a predetermined signal level and at least one second virtual waveform primitive comprising a signal edge. The training data set is formed and comprises a predetermined number of generated virtual waveform primitives including first virtual waveform primitives and second virtual waveform primitives. Each virtual waveform primitive comprises a sequence of time and amplitude discrete values. The training data set is used for training the signal classification neural network.

First claim

The invention claimed is:

1. A method for providing a training data set used for training a signal classification neural network,
the method comprising the steps of:
(a) generating at least one first virtual waveform primitive comprising a predetermined signal level and at least one second virtual waveform primitive comprising a signal edge;
(b) forming the training data set comprising a predetermined number of generated virtual waveform primitives including first virtual waveform primitives and second virtual waveform primitives,
wherein each virtual waveform primitive comprises a sequence of time and amplitude discrete values and
(c) using the training data set for training the signal classification neural network.

2. The method according to claim 1 wherein the first virtual waveform primitive comprises
a sequence consisting of a number of time and amplitude discrete values representing a specific constant signal level, in particular a constant logical signal level including a high signal level, a low signal level, a top signal level or a zero signal level.

3. The method according to claim 1 wherein the second virtual waveform primitive comprises
a sequence consisting of a number of time and amplitude discrete values representing a type of a signal edge, in particular a fast rising signal edge, a fast falling signal edge, a slow rising signal edge or a slow falling signal edge.

4. The method according to claim 1 wherein further virtual waveform primitives are generated to form part of the training data set,
wherein each generated further virtual waveform primitive comprises a sequence of time and amplitude discrete values representing a specific time-dependent function, in particular a polynomial function or a transcendental function.

5. The method according to claim 1 wherein the number of generated virtual waveform primitives forming said training data set matches a probability distribution of a signal to be classified by the trained signal classification network.

6. A method for performing a signal classification of a signal applied to a signal classification neural network trained with a training data set comprising a predetermined number of virtual waveform primitives each comprising a sequence of time and amplitude discrete values representing a time-dependent function, wherein the signal applied to the trained signal classification neural network is read from a data acquisition memory of a signal analyzing apparatus.

7. A method for performing a signal classification of a signal applied to a signal classification neural network trained with a training data set comprising a predetermined number of virtual waveform primitives each comprising a sequence of time and amplitude discrete values representing a time-dependent function, wherein a classification result calculated by the trained signal classification neural network in response to the applied signal comprises at least one classification signal indicating a signal portion where the applied signal comprises a specific signal level or comprises a specific type of signal edge.

8. The method according to claim 7 wherein the classification signal comprises
a first classification signal indicating where the signal data of the signal applied to the trained signal classification neural network comprises a specific signal level and/or
a second classification signal indicating where the signal data of the signal applied to the trained signal classification neural network comprises a specific type of signal edge.

9. A signal analyzing apparatus, the signal analyzing apparatus comprising
a signal acquisition memory adapted to store data samples of at least one received signal; and
an assistance system for classifying the received signal having a signal classification neural network trained with a training data set comprising a predetermined number of virtual waveform primitives and used to classify the received signal by processing the data samples of the received signal read from the signal acquisition memory of said signal acquisition apparatus.

10. The signal analyzing apparatus of claim 9 wherein the signal analyzing apparatus 1 comprises a digital oscilloscope.

Assignees

• Rohde & Schwarz

Inventors

• Werner Andreas

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title