Online Neural Network Reconfiguration via Genetic Algorithms
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Solution Overview
Problem
Current techniques for constructing and optimizing deep neural networks are inefficient and impractical due to their manual nature, making it difficult to adapt to increasing complexity and dynamic environments.
Innovation Solution
A system utilizing a hybrid logic library and a custom multi-objective genetic-based algorithm for online reconfiguration of neural networks, allowing for real-time monitoring and adaptation of neural network architecture, interconnectivity, and training, enabling continuous optimization and performance enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of neural network elements is used, then the neural network can be constructed with human expertise, but the process becomes increasingly difficult and impracticable as the structure increases in depth and complexity
Solution Approach 1:
The system employs automated algorithms that enable the neural network to self-construct and self-optimize without manual intervention. The algorithm automatically selects building blocks, determines network architecture, and performs reconfiguration based on performance metrics, replacing manual expertise with autonomous computational processes.
Solution Approach 2:
The patent replaces manual mechanical processes of neural network construction with automated computational algorithms. The system uses computer-executable instructions to automatically generate, train, and reconfigure neural networks, substituting human manual operations with algorithmic processes.
2Reliability
If conventional methods are used for neural network optimization, then the initial network can be trained, but there is no efficient way to optimize performance once implemented into production environment
Solution Approach 1:
The system implements dynamic reconfiguration of neural networks in real-time based on performance monitoring. The algorithm continuously adjusts network architecture, building block selection, and training parameters during production operation, transforming static neural networks into dynamically adaptable systems that optimize performance autonomously.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors neural network performance metrics during production operation and uses this information to trigger automated reconfiguration. The algorithm receives feedback on performance degradation or optimization opportunities and automatically initiates retraining and architectural modifications to maintain or improve performance.
3Adaptability or versatility
If the neural network architecture is fixed after construction, then the initial training can be completed, but the system cannot adapt to changing data patterns or improve performance over time
Solution Approach 1:
The system performs preliminary actions by pre-defining a library of building blocks and reconfiguration strategies before production deployment. The algorithm prepares multiple potential architectural configurations and optimization paths in advance, enabling rapid adaptation when performance improvement opportunities are detected during operation.
Data Source
AI summary
The present disclosure is directed to a novel system for performing online reconfiguration of a neural network. Once a neural network has been implemented into a production environment, the system may use underlying construction logic to perform an in-situ reconfiguration of neural network elements while the neural network is live. The system may accomplish the reconfiguration by modifying the architecture of the neural network and/or performing adversarial training and/or retraining. In this way, the system may provide a way increase the performance of the neural network over time along one or more performance parameters or metrics.