Neural Network Core System Using Non-Linear PCA and Tree Structures
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Solution Overview
Problem
Traditional artificial intelligence decision-making systems using linear data structures in neural networks produce non-distinguishable and low-value decision results, limiting their usability and practicality across various scenarios.
Innovation Solution
An artificial intelligence decision-making neuro network core system that combines non-linear analysis and feedback mechanisms, utilizing an electronic device with modules for data preprocessing, weight computation, non-linear computing, and data tuning to generate decision results adaptable to diverse scenarios, incorporating an unsupervised neural network interface, asymmetric hidden layers, and non-linear PCA modules for enhanced decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional linear data structure is used in neural network, then the system is simple and easy to implement, but the decision results are non-distinguishable and have low value
Solution Approach 1:
The patent applies non-linear data structures (curved/branching tree structures) instead of traditional linear (straight) data structures in the neural network. This curvature/non-linearity transformation enables the system to capture complex patterns and relationships in data, producing distinguishable and high-value decision results while maintaining implementation feasibility through systematic processing steps.
2Device complexity
If traditional linear neural network is used, then the system structure is simple, but the decision results have low reference and usability
Solution Approach 1:
The patent segments the data processing into distinct functional modules: data preprocessing module, tree-structure construction module, weight computation module, non-linear computing module, and feedback tuning module. This segmentation allows each module to specialize in specific tasks, enhancing the system's adaptability to different scenarios while keeping the overall structure organized and manageable.
Solution Approach 2:
The patent introduces dynamic feedback mechanisms where decision results are tuned and adjusted based on feedback information. The system dynamically adapts its processing through feedback loops that refine the non-linear computing results, enabling the decision-making system to adjust to various scenarios and improve result usability rather than following a static linear path.
3Loss of information
If non-linear analysis and feedback mechanism are combined, then the decision results accommodate various scenarios and have high value, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the output of the non-linear computing is fed back through a tuning module that adjusts and refines the decision results. This feedback loop enables the system to accommodate various scenarios by continuously refining its outputs based on performance, thereby maintaining high decision result value while managing complexity through structured feedback processing.
Solution Approach 2:
The patent combines multiple processing approaches (non-linear analysis, tree-structure data organization, weight computation, and feedback tuning) into a composite system architecture. This composite structure integrates different functional elements that work together to produce high-value decision results, similar to how composite materials combine different properties to achieve superior performance while managing overall system complexity.
Data Source
AI summary
Artificial intelligence decision making neuro network core system and information processing method using the same include an electronic device linking to a unsupervised neural network interface module, a asymmetric hidden layers input module linking to the unsupervised neural network interface module and a neuron module formed with tree-structured data, a layered weight parameter module linking to the neuron module formed with tree-structured data and an non-linear PCA (Principal Component Analysis) module, an input module of the lead backpropagation unit linking to the non-linear PCA module and a tuning module, an output module of the lead backpropagation unit linking to tuning module and the non-linear PCA module; when the electronic device receives raw data, processing and learning the raw data via all the modules, and updating programs to generate decision results that accommodate a variety of scenarios, in order to elevate the reference value and practicality of the decision result.