A framework for model maintenance using kernel-based forward propagating neural networks

Abstract

Deep learning models possess limited flexibility in computational burden in model adaptation owing to the conventional use of backpropagation for model training. To address this problem, we propose an alternate training methodology inspired by the forward–forward algorithm originally designed for classification tasks. We extend this concept through a kernel-based modification, enabling its application to regression tasks, which are commonly encountered in process system modeling. Our proposed Kernel-based Forward Propagating Neural Network (K-FP-NN) eliminates backpropagation, using layer-wise updates for better adaptability. We introduce a real-time (RT) updating framework, RT-K-FP-NN, to continuously refine model parameters with new data. Results indicate that when applied to model predictive control of a continuous stirred tank reactor (CSTR) system, our approach updates the model within 100 s, achieving better performance metrics compared to backpropagation-based real-time models, which require 326 s. This framework can be applied to various dynamic systems, enhancing real-time decision-making by improving predictive accuracy and system adaptability.