Neural Network Architecture Adaptation for Real-Time Data Pattern Changes
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Solution Overview
Problem
Current deep learning techniques are limited by their reliance on static training data, making them ineffective in adapting to changing real-world environments, particularly in applications like cybersecurity and intrusion detection where subtle data changes are critical.
Innovation Solution
An incremental state-based learning system that monitors data streams, assesses changes, and reconfigures neural network architectures in response to environmental conditions by modifying parameters such as the number of neurons, weights, and activation functions, allowing the system to adapt without complete retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static training data is used for neural network optimization, then the network can be trained efficiently with large data sources, but the system cannot adapt to changing real-world environments or detect subtle data changes in real-time
Solution Approach 1:
The patent implements dynamic adaptation by allowing the neural network to modify its architecture and parameters in real-time based on incoming data streams. The system transitions from static training to dynamic learning by incorporating incremental updates that adjust weights, add/remove neurons, and modify activation functions based on detected patterns in the data stream, enabling continuous adaptation to changing environments.
Solution Approach 2:
The system changes key neural network parameters dynamically including learning rates, network depth, width, neuron counts, and weight values based on the detected state of the data stream. This parameter adaptation allows the network to respond to subtle changes in the environment by adjusting its sensitivity and structural characteristics in real-time.
2Adaptability or versatility
If the neural network architecture is fixed, then the system structure is simple and stable, but the system cannot learn from new environmental data or respond to changing conditions
Solution Approach 1:
The patent segments the neural network into modular components that can be independently modified, including individual neurons, layers, and functional blocks. This segmentation allows the system to make targeted architectural changes (adding/removing specific neurons or layers) without restructuring the entire network, thereby managing complexity while enabling adaptability.
Solution Approach 2:
The network architecture transitions from fixed to dynamic, where the structure can grow or shrink based on learning needs. The system dynamically adds or removes neurons and layers, adjusts connectivity patterns, and modifies architectural parameters based on the detected data patterns and system state, allowing flexible adaptation without permanent structural commitment.
3Reliability
If complete retraining is performed to adapt to new conditions, then the system achieves high accuracy, but the process is time-consuming and computationally expensive
Solution Approach 1:
The system performs preliminary adaptation through incremental learning mechanisms that continuously update the network based on incoming data streams. By maintaining a state-aware learning process that pre-adjusts parameters and structures in response to detected patterns, the system prepares the network for future detection tasks without requiring complete retraining, thus reducing time loss while maintaining accuracy.
Solution Approach 2:
The patent implements continuous learning where the neural network constantly processes and learns from the data stream without interruption. This continuous useful action allows the system to maintain and improve detection accuracy through ongoing incremental updates rather than periodic complete retraining cycles, significantly reducing the time and computational resources required.
4Speed
If the learning rate is high for rapid adaptation, then the system responds quickly to new data, but the system may overshoot optimal solutions and reduce stability
Solution Approach 1:
The learning rate is implemented as a dynamic parameter that automatically adjusts based on the system state and detected patterns. The system increases the learning rate when rapid adaptation is needed (e.g., detecting novel patterns) and decreases it when stability is required (e.g., fine-tuning known patterns), thereby balancing speed and stability adaptively rather than using a fixed rate.
Solution Approach 2:
The system changes the learning rate parameter dynamically based on the detected data stream characteristics and system performance. This parameter adaptation allows the learning process to be both fast and stable by adjusting the step size according to the current learning phase and detected patterns, preventing overshooting while maintaining rapid response capability.
Data Source
AI summary
A system for reconfiguring neural network architecture responsive to a system state is provided. A controller for modifying a neural network learning engine is configured to monitor a data stream having a data pattern by comparing the data pattern to a trained data pattern; identify a change in the data pattern of the data stream; determine a state of the neural network learning engine, the state defining one or more neural network parameters for monitoring the data stream with the neural network learning engine; and in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters.