FIR Filter Parameter Tuning With Genetic Adaptation
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Solution Overview
Problem
Existing finite impulse response (FIR) filters require manual adjustment for each configuration change, which is impractical due to the large number of supported configurations, especially in versatile computers used in environments like aircraft, necessitating a more flexible and automated adjustment method.
Innovation Solution
A method using a genetic algorithm to automatically determine a sequence of sinusoidal band-stop filters with configuration parameters, involving generation, evaluation, crossover, and mutation of individuals to adapt the filter to new configurations without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of FIR filter is used for each configuration, then filtering performance can be optimized, but the time and complexity increase significantly with hundreds of configurations
Solution Approach 1:
The system performs self-adjustment of filter parameters through automatic generation and evaluation of configuration parameters, eliminating the need for manual intervention. The computer automatically determines optimal filter settings based on signal characteristics and configuration requirements.
Solution Approach 2:
The invention automatically changes filter parameters (cutoff frequencies, bandwidths, orders) based on evaluated configuration parameters, allowing the filter to adapt to different configurations without manual reconfiguration. This enables rapid parameter adjustment across hundreds of configurations.
2Reliability
If FIR filter is designed for optimal operation with specific computer and signals, then performance is maximized, but flexibility for new configurations is reduced
Solution Approach 1:
The filter design methodology becomes universal by automatically generating configuration parameters that work across multiple computer types and signal characteristics. The system evaluates and adapts parameters to serve multiple functions and configurations rather than being optimized for a single specific setup.
Solution Approach 2:
The filter configuration transitions from static (fixed for specific computer) to dynamic (automatically adjustable for any configuration). The system can dynamically determine appropriate parameters based on the specific computer and signal characteristics encountered.
3Adaptability or versatility
If versatile computer with software configuration is used, then flexibility increases, but automatic filter adjustment capability is lost
Solution Approach 1:
The system implements feedback loops where configuration parameters are generated, evaluated against performance criteria, and automatically adjusted based on evaluation results. This closed-loop approach restores automation to the filter adjustment process in versatile computers.
Solution Approach 2:
The manual mechanical adjustment process is replaced with an automated computational system that generates and evaluates configuration parameters algorithmically, enabling automatic adaptation in software-configurable computers.
Data Source
AI summary
A method for determining a finite impulse response filter, including generation of a plurality of individuals presenting configuration parameters, and cyclic implementation of the following operations in relation to each current population of configuration parameters: evaluation by an objective function of each individual, evaluation by at least one constraint function of each individual, selection of a group of individuals among all the individuals based on their evaluations by the objective function and the constraint function and formation of parent individuals, crossover of parent individuals to form a new population, mutation of individuals in the new population, and verification of a stopping criterion.