The Regression Learner app lets you explore your data, select features, specify validation schemes, optimize hyperparameters, and assess model performance to predict your data without the need of writing any code.

You can export regression models to the MATLAB® workspace or generate MATLAB code to integrate models into applications.

The app takes menial workflows and tasks like setting up the validation, picking the relevant predictors, and picking the optimal model and tuning it and makes them interactive, saving you time. Giving you full transparency and automates the workflow by generating codes automatically.

More Autonomy = More Sensors = More Raw Data to Visualize and Process

computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

Using MATLAB, engineers and other domain experts have deployed thousands of applications for predictive maintenance, sensor analytics, finance, and communication electronics.

The design and analysis of Analog/Mixed Signal Designs have become increasingly challenging with analog impairment effects in modern semiconductor technology nodes and with the integration of complex embedded digital signal processing and control algorithms. Thus, accurate modeling and rapid system-level simulation have become extremely important to be able to verify these designs before they go into production. In this webinar, we show how MathWorks tools can be used in modeling and simulating mixed-signal algorithms in different levels of abstraction. This session will demonstrate:

• the ability to describe analog parts of the system in Simulink also using linear and switched mode electrical networks.
• the ability to co-simulate together a design partially described in Simulink and Cadence® Incisive® (digital) and Virtuoso® AMS Designer (analog).
• using automatic to integrate the behavior of Simulink subsystems in the Cadence SystemVerilog workflow.
• the integration of MATLAB in Cadence Virtuoso ADE Explorer and Assembler to visualize and analyze the transistor-level simulation data from circuit verification. Also, we show comprehensive examples from the Mixed Signal Example Library, which provides a starting point to build a new design.

Photovoltaic solar power technology is being pushed to the forefront as a viable alternative source of renewable energy. Addressing the key design challenges involved in the development and implementation of these systems requires simulation tools capable of not only modeling and analyzing the behavior of diverse electronic components, but also the ability to combine them with the associated control algorithms and evaluate the full system performance.

There are many ordinary planning apps inside the market. A few assist their customers plan their customers plan. A few assist their customer’s planned trips by means of using maps to discover the shortest path. Sometimes the usual user for maps that they know to be a faster route tends to be longer travel time. Other direction making plan apps tried to triumph over this issue by means of having their customers switch on GPS. Based at the amassed users of GPS in a specific region, the app can expect a high number of users. Similarly, some apps also comprise traffic reviews and tweets into their optimised version.

These strategies depend upon the consumer’s approval to show their GPS and tweeting about site visitors’ situation. Some customers may wish to use the app in an offline mode and do not turn on their GPS because of privacy worries, and we cannot rely on users to continually tweet approximate traffic situation. Categorisation of site visitor’s density are subjective. One person’s definition of heavy traffic is sometimes just an ordinary day for everyday visitors.

By utilising the LTA traffic cameras to measure traffic density, we provide an addition method that is not dependent on users’ GPS and reports. The intention is not always to update course planning app, but rather to offer an assistance that can be used to enhance the existing path making plans.

Reinforcement learning is getting a lot of attention lately. People are excited about its potential to solve complex problems in areas such as robotics and automated driving, where traditional control methods can be challenging to use.

In addition to deep neural nets to represent the policy, reinforcement learning lends itself to control problems because its training incorporates repeated exploration of the environment. As such, exploration is time-consuming and costly, or dangerous when done with actual hardware—a simulation model is often used to represent the environment.

In this talk, we provide an overview of reinforcement learning and its application to teaching a robot to walk. We discuss the differences between reinforcement learning and traditional control methods.

Predictive maintenance makes a lot of promises, from reducing machine downtime and eliminating unnecessary maintenance to adding revenue streams for equipment vendors with aftermarket services.  These benefits are eminently achievable as long as you keep engineering and business challenges from getting in the way.