Self-Adjusting Fuzzy Logic Control Using Clustered Input History
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Solution Overview
Problem
Existing fuzzy logic applications do not account for dynamic changes in input variables, leading to inefficiencies as conditions evolve over time.
Innovation Solution
A self-adjusting fuzzy logic system that generates new fuzzy set definitions using historical input values and unsupervised learning algorithms, such as K-Means clustering, to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static fuzzy set definitions are used in traditional fuzzy logic applications, then the system structure remains simple and easy to implement, but the system cannot adapt to dynamic changes in input variables over time
Solution Approach 1:
The patent implements dynamic fuzzy set definitions that automatically update based on clustering analysis of historical input data. The system transitions from static membership functions to dynamic ones that adapt to changing conditions, directly resolving the contradiction between adaptability and complexity by making the system structure itself dynamic rather than static
Solution Approach 2:
The system performs self-adjustment through automated clustering algorithms that analyze historical data and regenerate fuzzy set definitions without external intervention. This self-service mechanism allows the system to adapt to dynamic changes while maintaining operational simplicity, as the adaptation process occurs autonomously based on accumulated data
2Measurement precision
If fuzzy set definitions are updated using historical data and clustering algorithms, then the system accuracy and responsiveness improve, but the computational complexity and processing time increase
Solution Approach 1:
The system performs clustering analysis and fuzzy set regeneration in advance during periods when computational resources are available, preparing updated definitions before they are needed for control decisions. This preliminary action allows the system to maintain high accuracy while managing computational complexity by performing intensive calculations proactively rather than reactively
Solution Approach 2:
The patent implements periodic updates of fuzzy set definitions based on accumulated historical data, rather than continuous real-time recalibration. This periodic approach balances accuracy improvements with computational efficiency, allowing the system to maintain precision while avoiding excessive computational overhead by updating at appropriate intervals
3Adaptability or versatility
If the system continuously adapts to new data, then the responsiveness to changing conditions improves, but the stability of existing control patterns may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms that monitor both the accumulated historical data and the performance of current fuzzy set definitions. This feedback allows the system to determine when adaptation is necessary while maintaining stability during periods when existing definitions remain effective, resolving the contradiction by making adaptation conditional rather than continuous
Solution Approach 2:
The patent implements controlled parameter changes in the fuzzy set definitions based on statistical analysis of historical data distribution. Rather than arbitrary changes, the system modifies membership function parameters systematically based on data-driven insights, ensuring that adaptability occurs through measured, stable transitions rather than abrupt changes that would compromise control stability
Data Source
AI summary
A computer hardware fuzzy logic system includes a controlled system and a fuzzy logic application configured to control the controlled system. A dataset defined by a period of time is retrieved from a store of historical input values for the controlled system. K clusters are generated from the dataset, and new fuzzy set definitions are generated for the K clusters. The fuzzy logic application updates old fuzzy set definitions with the new fuzzy set definitions. The fuzzy logic application also generates variable adjustments to the control system using the new fuzzy set definitions and received input values for the controlled system. The controlled system is modified using the variable adjustments.