Cascading Linkage Machine for Fuzzy Signal Matching
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Solution Overview
Problem
In complex computing networks, there is a need to match and link input signals with stored signals in a database, but existing technologies face challenges in comparing and linking signals with different attributes due to lack of compatibility and matching techniques.
Innovation Solution
An intelligent cascading linkage machine is introduced, equipped with a communication interface, processor, and power provisioning, which transforms input signals into comparable signals by modifying, adding, or deleting attributes, and uses multiple matching techniques, including fuzzy matching, to link signals with stored signals in the database, while calibrating and adjusting matching techniques based on linking strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple matching techniques are implemented to improve signal matching accuracy, then matching precision is improved, but device complexity increases
Solution Approach 1:
The matching system is divided into multiple independent matching techniques (exact matching, fuzzy matching, partial matching) that operate in a cascaded sequence. Each matching technique handles specific types of signal comparisons, allowing the system to achieve high matching accuracy across diverse signal types without requiring a single overly complex matching algorithm.
Solution Approach 2:
The system dynamically selects and cascades through different matching techniques based on the characteristics of the input signals and database signals. The matching process adapts by progressing from simpler exact matching to more complex fuzzy matching only when necessary, optimizing both accuracy and computational efficiency.
2Adaptability or versatility
If signal transformation operations are performed to make signals comparable, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary signal transformation operations to standardize input signals before they enter the matching process. By pre-modifying, adding, or deleting attributes to make signals comparable in advance, the system reduces the complexity of subsequent matching operations and avoids time-consuming transformations during the actual matching phase.
Solution Approach 2:
The system changes signal parameters by modifying attributes (adding, deleting, or modifying signal components) to transform incompatible signals into comparable formats. This parameter transformation enables the system to handle diverse signal types with different structures while maintaining efficient matching performance.
Data Source
AI summary
This disclosure is directed to an intelligent cascading linkage machine for transforming input signals into comparable signals, and cascading through matching operations, including but not limited to a fuzzy matching comparison technique, to link transformed input signals (comparable signals) to those stored signals in a database which match it. The fuzzy matching technique may use a random forest processing technique and/or a logistic regression technique. Also, the machine is able to calibrate its matching technique, based on the linking of a comparable or input signal to a stored signal in a database, in order to calculate an accuracy indicator.