Advantages of Artificial Intelligence (AI)

Our artificial intelligence and machine learning technology provides controllers with cognitive skills such as memory, attention, perception, understanding, and problem solving. In this way, the controller becomes an artificial brain with the ability to learn and perceive the environment around it.

#### Learning

During a defined period of time, the mathematical controller learns the properties of the process, the frequency of failures, anomalies, the behavior of the actuators and their relationship with the measured values of the field sensors, etc. By obtaining this information and using mathematical algorithms, a mathematical model is created, which accurately represents the process.

#### Optimization

The math controller optimizes the use of its resources and only uses the minimum necessary to achieve the expected quality. The AI controller requires little effort and minimizes unnecessary actuation of the actuators.

#### Prevention

The math controller prevents errors from occurring and indicates previous alarms by having predictive control capabilities.

##### Artificial intelligence elements

We achieve artificial intelligence from the use of artificial neural networks and linear systems of optimal control.

#### Artificial human ability

A neural network is a computer model whose layered structure is similar to the network structure of neurons in the brain with layers of connected nodes. A neural network can learn from data so that it can be trained to recognize patterns, classify data, and predict future events.

##### Artificial neural networks

Our control algorithm has the following capabilities. This algorithm is implemented in our mathematical controller.

#### Network structure

Our neural network is inspired by biological nervous systems. It consists of an input layer, one or more hidden layers, and an output layer. The layers are connected to each other by neurons; each layer takes the output of the previous layer as input.

#### Configuration and Simulation

The user can configure the neural network and simulate its behavior with real data from the process. These are obtained through the industrial network of the plant.

#### Connectivity

Our math controller connects to your process through various field networks, including Profinet.

#### Neural Algorithm

Our neural algorithm consists of supervised and unsupervised learning, classification, regression, pattern recognition, and clustering techniques. The algorithms are based on differential equations and can be configured.

#### Multi-Instance operation mode

Our multi-instance math controller has the ability to work in learning mode, simulation mode, and control mode simultaneously. Instances with real-time communication interfaces.

#### Deep & Machine Learning

Our algorithms have deep learning and machine learning models. Algorithms based on differential equations.

Optimal Control System

Our artificial intelligence and machine learning technology provides controllers with cognitive skills such as memory, attention, perception, understanding, and problem solving. In this way, the controller becomes an artificial brain with the ability to learn and perceive the environment around it.

#### Systems identification

The system’s digitizing and identification unit processes incoming data regardless of system linearity or non-linearity, as well as time or frequency domain using parametric methods.

#### Mathematical modeling

The mathematical modeling unit develops a model in the form of equations of state or transfer functions using combined autoregressive techniques and differential equations representative of previously collected and processed process data.

#### Optimal control & Minimization of costs

The parameterization unit of the artificial intelligence controller generates the optimal control law based on the minimization of the cost function and the optimization of resources (economy, energy and time).