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virtual sensor modeling

estimate signals of interest that a physical sensor cannot directly measure, or when a physical sensor adds too much cost and complexity to the design.

create and compare virtual sensor models using different deep learning and machine learning architectures such as fully connected layers, long short-term memory (lstm) layers, and support vector machines
import ai models created in tensorflow™ or pytorch^® for simulation and deployment with simulink
integrate, simulate, and test ai-based virtual sensors with the rest of the system
compress ai-based virtual sensor models and deploy them to microcontrollers and ecus using library-free c code generation
adapt virtual sensor models to process data in real-time using incremental learning

customer stories and case studies

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examples

system identification and rom

create ai-based models of nonlinear dynamic systems by using measured or generated data.

create ai-based dynamic models from measured data using the system identification app
improve model quality by combining insights about the physics of the system with ai techniques using nonlinear model identification, such as neural state space, nonlinear arx, and other model architectures
reuse third-party fem, fea, and cfd models for control design and system development in simulink by creating ai-based reduced-order models
use the reduced order modeler app to set up design of experiments (doe), generate training data, and build upon preconfigured templates to train and evaluate suitable ai models
bring the reduced model in simulink for running desktop simulations and hardware-in-the-loop testing, or export reduced-order models for use outside of simulink via functional mock-up units (fmus)

customer stories and case studies

videos

examples

reduced order modeling

reinforcement learning

train intelligent agents through repeated trial-and-error interactions with dynamic environments modeled in simulink.

select from out-of-the-box algorithms and integrate them into simulink with the rl agent block for training
use reinforcement learning designer to interactively design, train, and simulate agents
run system-level testing and deploy trained agents to embedded devices

customer stories and case studies

krones ag builds reinforcement learning–based process control in the blow molder contiloop ai for pet and rpet bottles

videos

examples

humanoid walker

why matlab and simulink for designing ai into engineered systems?

integrate and simulate ai models
with the rest of the system

integrate ai models directly into your system-level model for simulations.
simulate system behavior by running ai algorithms with other components of the system, including physical systems, environment models, closed-loop control algorithms, and supervisory logic.

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deep learning blocks, machine learning blocks, reinforcement learning blocks, and nonlinear model identification blocks in simulink
integrating ai into system-level design — ebook

achieve safety and reliability of
ai-enabled systems in operation

combine data-driven, simulation-based testing with formal verification techniques for neural networks.
ensure equivalence of behavior through back-to-back testing.
maintain traceability between requirements, design, and test.

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generate code from ai models
to target different hardware

generate and deploy c/c , cuda^®, and hdl code from deep learning or machine models that runs on supported target hardware.

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manage deployment trade-offs
of embedded ai

profile model size, speed, and accuracy in simulation and code.
compare differences in performance of different ai models and ai versus non-ai models.
assess impact of model compression.
leverage results of analysis to inform model selection, make design decisions, and fine-tune model behavior.

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ai with model-凯发k8网页登录

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

virtual sensor modeling

system identification and
reduced order modeling (rom)

reinforcement learning

virtual sensor modeling

system identification and rom

reinforcement learning

why matlab and simulink for designing ai into engineered systems?

integrate and simulate ai models
with the rest of the system

achieve safety and reliability of
ai-enabled systems in operation

generate code from ai models
to target different hardware

manage deployment trade-offs
of embedded ai

products

have questions?

contact ai for model-based design experts

ai with model-凯发k8网页登录

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

apply artificial intelligence (ai) techniques to the design of engineered systems

virtual sensor modeling

system identification and reduced order modeling (rom)

reinforcement learning

virtual sensor modeling

system identification and rom

reinforcement learning

why matlab and simulink for designing ai into engineered systems?

integrate and simulate ai models with the rest of the system

achieve safety and reliability of ai-enabled systems in operation

generate code from ai models to target different hardware

manage deployment trade-offs of embedded ai

products

have questions?

wechat

system identification and
reduced order modeling (rom)

integrate and simulate ai models
with the rest of the system

achieve safety and reliability of
ai-enabled systems in operation

generate code from ai models
to target different hardware

manage deployment trade-offs
of embedded ai