Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor

Abstract

This paper focuses on two types of neural networkbased Hammerstein model identification methods for the acrylamide polymerization reaction of a batch reactor process. The first neural-based identification type formulates the weights of the multilayer network directly as parameters of the nonlinear static and linear dynamic blocks of the Hammerstein model and trains the weights using a gradient-based backpropagation algorithm. In the second identification type, the nonlinear static block of the Hammerstein model is framed as a single hidden-layer feedforward network and both nonlinear and linear block parameters are trained using an extreme learning machine, where the training procedure is exempted from gradient calculation. The primary focus of the paper is neural-based model identification of a complex nonlinear system, which facilitates ease of linear/nonlinear controller design with good learning speed and less computations. A future work toward the machine learning-based nonlinear model predictive controller implementation using the Jetson Orin Nano board is also described.

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

“…In such cases, more advanced models capable of handling nonlinear relationships are often required. SVMs with nonlinear kernel, can effectively capture nonlinear patterns in data from pilot plant BR. , …”

“…SVMs with nonlinear kernel, can effectively capture nonlinear patterns in data from pilot plant BR. 30 , 31 …”

A Model Based on Fuzzy Neural Networks for Sharing Digital Educational Resources in English

“…In recent years, the integration of fuzzy control and neural network techniques has garnered significant attention across diverse research fields owing to their effectiveness in modeling nonlinear systems. The fusion of these approaches has given rise to the development of a unified system known as a fuzzy neural network (FNN) or neuro-fuzzy system, offering a powerful tool for research and practical applications in diverse domains (Balakrishnan et al, 2023). This paper introduces an FNN-based model designed specifically for sharing digital English educational resources, with a focus on promoting effective mechanisms for comprehensive guidance in resource sharing.…”