Self-Constructing Neural Network via Hybrid Logic Library
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Solution Overview
Problem
The manual construction of deep neural networks becomes impractical due to their increasing complexity, limiting their scalability and efficiency, and requiring autonomous design and adaptation to dynamic environments and various use cases.
Innovation Solution
A self-constructing neural network system utilizing a hybrid logic library with adaptable building blocks and a custom multi-objective genetic-based algorithm for iterative optimization, enabling real-time reconfiguration and adaptation to achieve robustness, efficiency, and performance balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual construction methods are used for neural networks, then design control and understanding are maintained, but scalability and efficiency deteriorate as network depth and complexity increase
Solution Approach 1:
The system employs a self-constructing neural network architecture where the network automatically generates and optimizes its own structure through adversarial learning. The network serves itself by autonomously selecting building blocks from a hybrid logic library and configuring its architecture without manual intervention, thereby resolving the contradiction between construction efficiency and complexity management
Solution Approach 2:
The neural network is constructed using a hybrid logic library that segments the network into modular building blocks including traditional logic structures, memory structures, and learning structures. This segmentation allows the complex network to be built from manageable, pre-defined components, improving construction efficiency while maintaining structural organization
2Adaptability or versatility
If manual exploration of neural networks is used, then design precision is maintained, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The system implements dynamic adaptability through adversarial learning, where the neural network continuously evolves its architecture in response to changing environments and tasks. The network can dynamically reconfigure itself by selecting different building blocks from the hybrid logic library, enabling adaptation to dynamic environments while maintaining a degree of design control through the structured library approach
Solution Approach 2:
The system enables adaptability by allowing the neural network to change its structural parameters autonomously. Through the self-constructing mechanism, the network can modify its architecture, depth, and composition of building blocks based on performance requirements and environmental conditions, achieving versatility without complete loss of design precision
3Reliability
If deeper and more complex neural networks are constructed, then performance capability is improved, but manual construction feasibility deteriorates
Solution Approach 1:
The system resolves the feasibility issue by implementing self-construction, where the neural network automatically generates its own deep and complex architecture through adversarial learning. This eliminates the need for manual construction of complex networks while maintaining performance capability through autonomous optimization and selective assembly of building blocks from the hybrid logic library
Data Source
AI summary
The present disclosure is directed to a novel system for a self-constructing deep neural network. The system may comprise a hybrid logic library which contains the building structures needed to construct the neural network, which may include both traditional logic and memory structures as well as learning structures. In constructing the neural network from library structures, the system may use an algorithm to iteratively improve the performance of the neural network. In this way, the system may provide a way to generate complex neural networks that become increasingly optimized over time.