A neural network model, a method and modelling environment for configuring neural networks

The method according to one of the preceding claims comprising:

• storing the restriction matrix, and the associated weights and biases in an encrypted binary format with a digital watermark.

The method according to one of the preceding claims wherein, configuring the neural network (120) based on the changeability index comprises:

• restricting modification of the neural network (120) based on the restriction matrix (150);
• and
• validating operation of the neural network (120, 172, 182) in the industrial system based on conformance with the restriction matrix, wherein validating operation of the neural network (182) comprises validating designing of the neural network (182) based on conformance with the restriction matrix.

The method according to one of the preceding claims, further comprising:

• training a second neural network (182) based on the restriction matrix, the trained dataset and the predefined-parameters.

The method according to one of the preceding claims, further comprising:

• generating the trained dataset by labelling one or more training datasets with annotations indicating the critical features and connectivity of the hardware and software components based on the system architecture.

An architecture modelling environment (100, 300, 400) for configuring at least one neural network (120) used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components, the architecture modelling environment comprising:

• an IDE configured to enable collaborative configuration of the neural network (120), wherein the IDE is configured according to at least one of the method steps in claim 90.