Collective System Modeling for Noisy Dynamic Behavior Control
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Solution Overview
Problem
Existing methods for modeling and controlling complex dynamical systems, such as biological subjects, are computationally expensive and face challenges with noise-polluted data and exogenous uncertainties, leading to suboptimal modeling and control.
Innovation Solution
A method involving the formation of model units with parameters based on quantified system data, calculation of solution trajectories, determination of fitness values, and selection of model unit combinations to form a collective model, utilizing techniques like evolutionary algorithms and swarm optimization to enhance modeling and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional modeling methods are used to accurately model system behavior, then modeling accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the system into multiple interacting compartments or state variables, each with its own differential equations. This allows the complex system to be modeled through manageable subsystems that can be solved more efficiently than a monolithic model, while maintaining overall accuracy through the interaction terms between compartments.
Solution Approach 2:
The patent transforms the system of differential equations into the frequency domain using Fourier transforms, changing the parameter representation from time-domain functions to frequency-domain spectra. This parameter transformation enables more efficient computation of convolution operations and system responses, reducing computational cost while preserving modeling accuracy.
2Measurement precision
If detailed system data is collected to improve model accuracy, then modeling precision is improved, but the system becomes more sensitive to noise and uncertainties
Solution Approach 1:
The patent implements feedback mechanisms where model predictions are continuously compared with actual system measurements, and the discrepancy (error) is used to adjust model parameters. This feedback loop allows the model to adapt to noisy data by learning from actual system behavior, improving precision while filtering out noise through the iterative optimization process.
Solution Approach 2:
The patent performs preliminary data processing and filtering before feeding data into the model, and pre-computes system responses to common disturbances. This preliminary action reduces the impact of noise on the modeling process by preparing cleaned and pre-processed data, thereby improving modeling precision without amplifying noise sensitivity.
3Measurement precision
If complex models are used to capture system dynamics, then behavior prediction accuracy is improved, but model complexity and difficulty of control increases
Solution Approach 1:
The patent transforms the complex time-domain differential equations into frequency-domain algebraic equations through Fourier transforms. This parameter change simplifies the mathematical operations from solving differential equations to performing algebraic manipulations and spectral analysis, reducing model complexity while maintaining the ability to capture complex system dynamics and predict behavior accurately.
4Measurement precision
If more computational resources are allocated to modeling, then model accuracy is improved, but processing time increases
Solution Approach 1:
The patent changes the computational parameters by working in the frequency domain rather than the time domain. This transformation converts computationally intensive differential equation solving into more efficient spectral computations and convolution operations, improving model accuracy while significantly reducing processing time through the use of fast Fourier transform algorithms.
Data Source
AI summary
A method of modelling system behaviour of a physical system, the method including, in one or more electronic processing devices obtaining quantified system data measured for the physical system, the quantified system data being at least partially indicative of the system behaviour for at least a time period, forming at least one population of model units, each model unit including model parameters and at least part of a model, the model parameters being at least partially based on the quantified system data, each model including one or more mathematical equations for modelling system behaviour, for each model unit calculating at least one solution trajectory for at least part of the at least one time period; determining a fitness value based at least in part on the at least one solution trajectory; and, selecting a combination of model units using the fitness values of each model unit, the combination of model units representing a collective model that models the system behaviour.