Nonlinear System Identification for Interpretable Massive Data Compression
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Solution Overview
Problem
Existing system identification and data compression techniques struggle to accurately and efficiently capture complex, high-dimensional data systems with implicit relational structures, leading to computationally expensive, unintuitive, and non-actionable representations that fail to preserve interpretability and predictive capabilities.
Innovation Solution
A method for encoding complex relationships between variables using differential-type equations, allowing for efficient, lossless data compression while preserving interpretability and predictive accuracy, enabling real-time control and deep analysis of multimodal data systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If standard data compression algorithms are used, then data compression ratio is improved, but relational information and interpretability are lost
Solution Approach 1:
The patent extracts relational structures and implicit patterns from high-dimensional data while compressing it. The system identifies and separates the essential relational information from the raw data, preserving it in a compressed form that maintains both compression efficiency and interpretability.
Solution Approach 2:
The patent transforms data representation by changing parameters from raw high-dimensional values to compressed relational descriptors. This parameter transformation enables the data to be stored in a more efficient format while preserving the underlying relational structures through mathematical transformations.
2Measurement precision
If complex system identification techniques are used, then prediction accuracy is improved, but computational cost and complexity increase
Solution Approach 1:
The patent extracts only the essential relational structures and dynamical patterns from complex systems, rather than attempting to model every detail. This extraction approach maintains prediction accuracy by focusing on the critical relational information while significantly reducing system complexity.
Solution Approach 2:
The patent segments the complex system identification process into distinct stages: relational structure extraction, pattern identification, and model construction. This segmentation allows each stage to focus on specific tasks, improving overall efficiency while maintaining accuracy.
3Adaptability or versatility
If traditional system identification tools are used, then algebraic representations are obtained, but interpretability and actionable insight are reduced
Solution Approach 1:
The patent transforms algebraic representations into forms with enhanced interpretability by changing the parameterization approach. The system converts abstract algebraic relationships into more intuitive representations that maintain mathematical rigor while improving human understanding and actionable insight.
4Measurement precision
If high-dimensional data is processed with detailed analysis, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts essential relational patterns from high-dimensional data, reducing the data volume that requires detailed processing. By identifying and focusing on the critical relational structures, the system maintains prediction accuracy while significantly reducing the computational time and resources required.
Solution Approach 2:
The patent performs preliminary relational structure extraction and data compression before detailed analysis. This preliminary action prepares the data in a form that is more efficient for subsequent processing, reducing the overall computational burden while preserving the information needed for accurate predictions.
Data Source
AI summary
A method for compressing and encoding relational aspects of high-dimensional, potentially highly-nonlinear, possibly inter-dependent, data systems has been developed, providing the ability to efficiently encode, validate, interpret, enhance, and design improved devices, algorithms, and systems. This procedure additionally allows for the easy incorporation of analysis and identification procedures for understanding the ways in which complex systems operate, as well as the ability to control, compress, predict, simulate, assess, analyze, and describe these systems in concise and easily understandable ways.