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Updated: Jun 19, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Multiinnovation least-squares identification for system modeling.

Feng Ding¹, Peter X Liu, Guangjun Liu

¹Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada. fding@jiangnan.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 4, 2009

Summary

This summary is machine-generated.

A novel multi-innovation least-squares (MILS) algorithm enhances parameter estimation accuracy for linear regression models. This improved method uses multiple past innovations, ensuring consistent convergence and adaptability to missing data.

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Published on: November 8, 2019

Area of Science:

Control Engineering
Signal Processing
System Identification

Background:

Traditional Recursive Least-Squares (RLS) algorithms face limitations in parameter estimation accuracy for linear regression models.
Expanding innovation length is a key area for improving existing identification algorithms.

Purpose of the Study:

To introduce a Multi-Innovation Least-Squares (MILS) identification algorithm for enhanced parameter estimation.
To develop robust MILS variants for handling missing data and complex system models.

Main Methods:

The proposed MILS algorithm expands the innovation length in the RLS algorithm by incorporating past innovations (p > 1) for each iteration.
An interval-varying MILS algorithm is introduced to dynamically adjust intervals for missing measurement data.
An auxiliary-model-based MILS algorithm is derived for pseudolinear models with colored noises.

Main Results:

The MILS algorithm demonstrates improved parameter estimation accuracy compared to the traditional RLS algorithm.
Performance analysis and simulations confirm the consistent convergence of the proposed MILS algorithm.
The interval-varying and auxiliary-model-based MILS algorithms effectively address missing data and complex system structures.

Conclusions:

The MILS algorithm offers a significant advancement in parameter estimation for linear regression models.
The developed variants provide robust solutions for real-world applications with data imperfections and complex dynamics.
The algorithms were successfully applied to model an experimental water level control system, validating their practical utility.