Leveraging Feed-Forward Regression Neural Networks with Simio

For the average enterprise, an ideal work scenario is one where automated systems can be trusted to do the heavy lifting when it comes to making complex decisions and taking real-time actions.  Advancements in Industry 4.0, artificial intelligence, machine learning, and neural networks are currently bringing the vision of complete autonomous work to reality across the industrial sector.

Today, Simio integrates the use of Neural Networks to simplify complex modeling tasks to reduce the workload of project managers, technicians, and developers using simulation and digital twin modeling software. Here, Neural Networks refer to a type of machine learning algorithm that can be used to automate the process of defining custom logic and rules within complex modeling applications.

Simio Software leverages Neural Network regression models to build complex rule-based logic. Hence, eliminating the considerable human labor associated with defining complex rules associated with modeled processes and objects. Simio and its application of Neural Networks form a powerful symbiotic relationship – Simio leverages Neural Networks to automate difficult modeling tasks and Neural Networks can be trained to increase their accuracy levels using Simio and data-generated by Simio.

Use Cases for Implementing Neural Networks with Simio

Automating decision-making through extensive data analysis is the goal of Industry 4.0. Hence, a neural network’s ability to take in enterprise data sets and provide accurate insight to simplify decision-making is crucial when developing simulation and digital twin models to analyze complex industrial activities. Here is a non-exhaustive list of scenarios where Simio utilizes Neural Networks and extends its capabilities:

• Defining custom rule-based logic for complex decision-making simulation models to simplify the modeling process
• Generate extensive training data sets for training your neural network algorithms on 3rd party applications to improve accuracy
• Support for importing ONNX files of your Neural Networks for evaluation purposes
• Defining custom rule-based logic when modeling digital twin models of complex industrial processes