Self-Organizing Map Node Removal for Noise Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The ESOM learning process lacks a function to remove nodes, making it susceptible to noise and varying results due to parameter dependencies, particularly the path removal intervals.
Innovation Solution
A learning system that includes a control unit to create a self-organizing map by identifying neighboring nodes for input data, calculating distances, adding new nodes when distances exceed a maximum, and updating node positions and activation values based on distances, while also calculating activation rates to determine the presence or absence of nodes and paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ESOM learning process is used to create self-organizing map, then the map can be formed through incremental learning, but the process becomes susceptible to noise and isolated nodes due to lack of node removal function
Solution Approach 1:
The patent implements a node removal mechanism that discards isolated nodes and noise points from the self-organizing map during the learning process. By calculating distances between nodes and removing those that exceed a threshold, the system maintains the incremental learning capability while eliminating susceptibility to noise and isolated nodes, thus resolving the technical contradiction.
2Reliability
If path removal intervals are adjusted in ESOM, then noise can be reduced, but the results vary greatly and necessary paths may be removed
Solution Approach 1:
The patent employs a feedback mechanism where the system calculates activation rates for each node based on input data and dynamically adjusts node retention decisions. This feedback loop allows the system to reduce noise while preserving necessary paths automatically, eliminating the need for manual path removal interval adjustments and reducing parameter complexity.
Solution Approach 2:
The self-organizing map performs self-adjustment through automatic node removal based on calculated distances and activation rates. The system serves itself by identifying and removing unnecessary nodes without external parameter intervention, thereby reducing noise while maintaining necessary paths and simplifying the overall system complexity.
3Measurement precision
If nodes are added frequently to capture complex data distributions, then the map accuracy improves, but the complexity of parameter adjustments increases
Solution Approach 1:
The patent balances node addition and removal by implementing a dynamic node management system. Nodes are added to capture complex data distributions for improved accuracy, while simultaneous removal of isolated nodes prevents excessive complexity. This dual mechanism maintains map accuracy while automatically managing system complexity without requiring additional parameter adjustments.
Data Source
AI summary
A control unit of a support server identifies, with respect to input data, a nearby node among existing nodes. The control unit calculates the distance between the input data and the nearby node. If the distance is greater than a maximum distance, the control unit uses the input data to add a new node to the existing nodes, and updates the activity value and the age of the new node and the nearby node in accordance with the distance to the nearby node. The control unit generates a self-organizing map by using the activity value of each node and the activity value of each path to calculate an age-based degree of activity, and by determining the presence or absence of each node and each path according to the degree of activity.