Neural Network Teaching Signal Generation and Validity Verification
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Solution Overview
Problem
Existing methods for generating teaching signals for neural network models are limited, as they only produce predetermined signals and fail to handle inputs that cannot be processed correctly by hierarchical neural network models, making it difficult to achieve efficient signal processing.
Innovation Solution
A processing system that includes a receiving circuit, an expected signal generating circuit, a learning circuit, an inference circuit, and a validity verification circuit, which automatically generates teaching signals and performs similarity calculations to ensure correct signal processing, allowing for adaptive learning and handling of defective signals by switching algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic teaching signal generation is implemented using existing methods, then manual setting effort is reduced, but the system cannot handle defective signals and is limited to predetermined signals only
Solution Approach 1:
The system introduces a validity verification circuit that provides feedback on the quality of generated teaching signals. When defective signals are detected through similarity comparison, the system automatically switches to alternative algorithms to generate valid teaching signals, ensuring continuous adaptability while maintaining automation.
Solution Approach 2:
The system dynamically switches between multiple algorithms based on the validity of generated teaching signals. This dynamic adaptation allows the system to handle defective signals by selecting appropriate algorithms, thereby improving versatility without sacrificing automation.
2Reliability
If hierarchical neural network models are used for signal processing, then structured learning is achieved, but the system fails to process certain input signals correctly
Solution Approach 1:
The system employs dynamic algorithm switching based on signal validity assessment. When the hierarchical neural network model fails to process certain inputs correctly, the system automatically switches to alternative algorithms, maintaining reliability while improving adaptability to various input signals.
Solution Approach 2:
The system changes processing parameters by switching between different algorithms based on the characteristics of input signals. This allows the system to maintain reliable processing for standard signals while adapting to handle diverse and challenging input signals effectively.
3Measurement precision
If manual algorithm setting is performed to improve processing accuracy, then signal processing precision is improved, but system complexity and setup time increase
Solution Approach 1:
The system performs self-configuration by automatically selecting and switching between algorithms based on the validity of generated teaching signals. This eliminates the need for manual algorithm setting while maintaining high processing accuracy, thereby reducing system complexity and setup time.
Solution Approach 2:
The validity verification circuit provides automatic feedback on processing accuracy, enabling the system to self-adjust algorithm selection without manual intervention. This feedback mechanism maintains measurement precision while eliminating the complexity of manual configuration.
4Device complexity
If predetermined teaching signals are generated, then system simplicity is maintained, but processing efficiency and adaptability are limited
Solution Approach 1:
The system dynamically switches between predetermined signals and alternatively generated teaching signals based on validity assessment. This dynamic approach maintains system simplicity while improving processing efficiency by automatically utilizing valid teaching signals when available.
Solution Approach 2:
The system changes the source of teaching signals based on validity parameters. When predetermined signals are insufficient or defective, the system automatically switches to generating teaching signals through alternative algorithms, thereby improving productivity without significantly increasing system complexity.
Data Source
AI summary
A processing system includes a receiving circuit 1 for receiving an input signal from an externally connected sensor, an expected signal generating circuit 4 for automatically generating a teaching signal for use in the learning circuit 5, a learning circuit 5 for calculating a weight value, a bias value, and the like of the neural network model to form an expected signal from the teaching signal generated by the expected signal generating circuit 4 and the signal from the receiving circuit 1, an inference circuit 2 for performing signal processing based on a learned model of the neural network model generated by the learning circuit 5, and a validity verification circuit 3′ for performing similarity calculation between an output signal of the inference circuit 2 and an expected signal for comparison.