And this could make weather forecasts more accurate.
However, researchers know only how reservoir computing works, not what goes on inside. The artificial neural networks in reservoir computing are constructed on mathematics, and it appears that all the system required to run more efficiently was a simplification. A team of researchers led by Daniel Gauthier, lead author of the study and professor of physics at The Ohio State University, was able to do just that, dramatically reducing the need for computing resources and saving significant time.
When the concept was put to the test on a forecasting task, it was discovered that the next-generation reservoir computing technique was clearly superior to others, according to the study published in the journalDepending on the data, the new approach proved to be 33 to 163 times faster. However, when the work objective was changed to favor accuracy, the new model was 1 million times faster.
"For our next-generation reservoir computing, there is almost no warming time needed," explained Gauthier,."Currently, scientists have to put in 1,000 or 10,000 data points or more to warm it up. And that's all data that is lost, that is not needed for the actual work. We only have to put in one or two or three data points."
Furthermore, the new technique was able to attain the same accuracy with only 28 neurons, as opposed to the 4,000 required by the current-generation model. "What’s exciting is that this next generation of reservoir computing takes what was already very good and makes it significantly more efficient," Gauthier stated. And it looks like this is only the beginning. The researchers plan to test the super-efficient neural network against more difficult tasks in the future, expanding the work to even more complex computer issues,"That’s an incredibly challenging problem to solve," Gauthier said.
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