ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting, or invades data privacy by revisiting used exemplars. Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data). The absolute memorization is demonstrated in the sense that class-incremental learning using ACIL given present data would give identical results to that from its joint-learning counterpart which consumes both present and historical samples. This equality is theoretically validated. Data privacy is ensured since no historical data are involved during the learning process. Empirical validations demonstrate ACIL's competitive accuracy performance with near-identical results for various incremental task settings (e.g., 5-50 phases). This also allows ACIL to outperform the state-of-the-art methods for large-phase scenarios (e.g., 25 and 50 phases).Comment: published in NeurIPS 202

A pattern-based approach to self-tuning process control

This thesis addresses the issues of performing closed-loop process identification under unknown load disturbances and reference step change, hence achieving a self-tuning controller that makes good use of the available on-line spatial-temporal information for control. A new method for process dynamic parameter identification utilizing the closed-loop response patterns is proposed. The main contribution is to be seen through our establishment of a formal basis for the pattern-based method for closed-loop process identification.Doctor of Philosophy (EEE

Expert auto-tuner for multivariable control applications

An expert auto-tuner for multivariable control applications is proposed. The main idea of the auto-tuning control methodology is to apply SISO (Single-Input-Single-Output) PID (Proportional, Integral and Derivative) controller with appropriate adaptation for the MIMO (Multi-Input-Multi-Output) environment. The reasons for choosing the PID controller include its robustness and its well balanced action to reduce offset and to improve response. More importantly, the PID controller has been widely accepted in the industry with its clear physical meaning of the controller parameters. An expert tuning processor is designed to operate on the PID controller to provide good tuning parameters based on the observed response.Master of Engineerin