AI Decision Core with Dynamic Neural Topology
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Solution Overview
Problem
Conventional artificial-intelligence decision-making systems with single neural network topologies struggle to adapt to diverse data types and features, leading to non-distinguishable training results and vulnerability to external neural network analysis.
Innovation Solution
An artificial-intelligence decision-making core system with a neural network that dynamically adjusts neuron nodes, employs non-linear analysis processes, and incorporates a residual-compensation mechanism, comprising an unsupervised neural-network interface module, neuro-computing sub-system, and residual-backpropagation sub-system to support various data types and enhance decision-making applicability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single type of neural network topology is used, then the system structure is simple, but the system cannot adapt to diverse data types and features
Solution Approach 1:
The patent implements dynamic topology adjustment by enabling the neural network to automatically change its structure (number of layers, neurons per layer, connection patterns) based on the characteristics of input data. This allows a single system to adapt to diverse data types without requiring multiple fixed topologies, resolving the contradiction between adaptability and structural simplicity.
Solution Approach 2:
The system changes structural parameters (topology configuration, neuron count, layer depth) dynamically according to data features. By making these parameters variable rather than fixed, the system achieves versatility across different data types while maintaining a unified base architecture, thus resolving the contradiction between adaptability and structural complexity.
2Manufacturing precision
If traditional linear data structures are used in neural networks, then the implementation is straightforward, but the training results are non-distinguishable and lack differentiation
Solution Approach 1:
The patent replaces traditional linear data structures with non-linear structures that can capture complex patterns and relationships in the data. This non-linearity introduces curvature and complexity into the data representation, enabling differentiated training results that can distinguish between different data types and features, thus resolving the contradiction between result differentiation and structural simplicity.
3Reliability
If the neural network topology is trained to fit specific data features, then the decision-making accuracy for that data type is improved, but the system becomes vulnerable to external neural network analysis and cracking
Solution Approach 1:
The patent implements dynamic topology adjustment that allows the neural network to change its structure based on input data characteristics. This dynamic adaptation means the network presents a different internal structure for each data type, making it difficult for external analysts to crack or reverse-engineer the system by studying its behavior on specific data types, thus resolving the contradiction between accuracy and security.
Solution Approach 2:
By dynamically changing structural parameters (layer counts, neuron configurations, connection patterns) based on data features, the system maintains high accuracy for each data type while preventing external analysis. The varying parameters create unique computational paths for different inputs, obscuring the underlying logic from external observers and resolving the security-accuracy trade-off.
Data Source
AI summary
An artificial-intelligence decision-making core system with neural network implements asymmetric hidden layers which is constructed by neural network with a dynamic neuron adjusting mechanism via making use of a device with computing unit and storage media, coupled to an independent feedback sub-system which operates a residual-compensation mechanism, thereby the core system can receive various trained data and performs a non-linear analysis process according to the output data derived from the asymmetric hidden layers of an unsupervised neural network, so as to derive individual and applicable decision-making data.