This modularized machine learning model improves the accuracy of deep neural network (DNN) models. The standard methodology for machine learning is through backpropagation (BP) algorithms. While backpropagation allowed tuning the parameters of multilayer neural networks directly from data in supervised mode, it also was responsible for weaknesses that made backpropagation less than optimal. These frameworks use digital signal processors and GPU units, which limit size and energy efficiency. With a global market for mobile traffic of about 47.6 million terabytes per month in 2020 and an estimated 220.8 million terabytes per month by 2026, there presents a need for innovative algorithms in machine learning to handle the size of the data at a sufficient rate.
Researchers at the University of Florida have developed a framework to adapt and identify Multiple Input Multiple Output (MIMO) large and multimodal engineering processing plants based on the theory of maximal correlation that produces modular training and has superior accuracy when compared to backpropagation, while avoiding the weaknesses of that technology (end to end training, lack of explainability). The Maximal Correlation Algorithm (MCA) estimates the statistical dependence of the samples.
Machine learning for system identification, online learning from data streams produced by sensors in large engineering plants
Maximal correlation algorithm is a new perspective for adaptive and learning systems that estimates the statistical dependence or relatedness between variables, without forming an error. Maximal correlation algorithm unifies the model’s mapping function and cost function and allows for modularized training of mappers with hidden layers as Multi-Layer Perceptrons (MLP). The machine-learning model uses adaptive signal filter analysis, employing all statistical information about the model outputs and the desired signals. Thus, researchers have established a new framework to adapt and identify MIMO systems based on statistical dependence.
