Application examples

Neuroblast's neural algorithms are the solution to a wide range of computational problems. Several examples are shown below, but they only begin to scratch the surface. Our algorithms are continuously growing ever smarter and ever more powerful. If your computational needs require more intelligence than your computers currently have, please contact us, and we'll see if Neuroblast's neural algorithms are the right solution for you.

Process Control
General-purpose no-tuning automatic control algorithms

The most common control algorithm in use today is the PID controller. Anyone who has ever tried to use one knows how hard they can be to implement, requiring the simultaneous optimization of two or sometimes three dynamically-coupled tuning parameters. Neuroblast created drop-in alternatives that provide much more power, with much greater ease.

The image on the left shows the disturbance-rejection performance of one of Neuroblast's controllers (in red), compared to an expert-designed PID controller (in grey). As usual, the Neuroblast controller was far more robust, returning the process to its setpoint nearly instantly. And yet while the PID controller had to be specially tuned for this specific process, the Neuroblast controller was simple plug and play.

Robotics
The coming revolution

The robotics industry is often said to be right now where the PC industry was 30 years ago, on the verge of exploding into a ubiquitous aspect of our everyday lives. But one thing is holding this industry back: the robots do not have brains.

Neuroblast can create the brains for many different kinds of robotic platforms. For example, the robot on the left, a Pololu 3pi, has five light sensors and two servo motors. Neuroblast developed a neural algorithm to control this robot, enabling it to steer and follow lines at high speed with complete autonomy.

Modeling and Prediction
Discover hidden trends

Neuroblast's neural algorithms excel at uncovering subtle relationships burried within complex and noisy data streams, and exploiting the predictive value of those relationships.

For example, the image on the left shows a typical trading session of an algorithm we created to place bets in a virtual market. The algorithm trades simultaneously on two separate securities (in black), and can go either long or short (in red; high lines and low lines, respectively). The virtual market is governed by over 80 dynamic equations, continuously randomly varying parameters, and heaps of random noise. For all the complexity and noise of the underlying process, the algorithm is a very successful trader, posting a typically healthy steadily increasing balance (in green).