Machine Vision Classification Using Differential Neural Networks
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Solution Overview
Problem
Current machine vision systems for industrial applications, particularly in vehicle manufacturing, require substantial computational resources due to complex neural networks, leading to inefficient training times and high costs, and are prone to overfitting and redundancy, making them unsuitable for real-time industrial deployment on lightweight hardware.
Innovation Solution
The system transforms initial neural networks into a differentiable form using series expansions and Chen-Fliess systems, allowing for optimization by reducing redundant parameters and using linear algebra techniques for training, resulting in a more efficient and lightweight implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex neural networks are used for image classification, then classification accuracy is improved, but computational resources and training time increase substantially
Solution Approach 1:
The patent transforms the neural network from a discrete, non-differentiable structure into a continuous, differentiable form by changing the mathematical parameters and representation. This allows the use of gradient-based optimization methods which reduce computational complexity while maintaining classification accuracy, directly resolving the contradiction between accuracy and computational burden
Solution Approach 2:
The patent replaces the traditional mechanical neural network architecture with a differential equation-based system. By substituting the discrete neuron activation mechanisms with continuous differential equations, the system achieves smoother optimization landscapes that require fewer computational resources while preserving the essential classification functionality
2Adaptability or versatility
If traditional neural networks are used, then they can learn from data, but they are prone to overfitting and redundancy
Solution Approach 1:
The patent extracts and removes redundant parameters and components from the neural network by transforming it into a differential equation form. This extraction process eliminates unnecessary complexity and reduces overfitting while retaining the core learning capability, thereby improving reliability without sacrificing adaptability
Solution Approach 2:
Instead of adding more parameters to improve learning, the patent inverts the approach by starting with a fully-parameterized neural network and systematically removing parameters through the differential equation transformation. This inversion strategy prevents overfitting from the outset while maintaining essential learning functions
3Measurement precision
If fully-connected neural networks are used with many neurons, then classification capability is improved, but training time and computational power requirements increase
Solution Approach 1:
The patent changes the fundamental parameters of the neural network by representing it as a differential equation with continuous variables. This parameter transformation allows for more efficient optimization algorithms that converge faster, reducing training time while maintaining the classification capability provided by the original fully-connected architecture
Data Source
AI summary
A machine vision system comprising receiving means configured to receive image data indicative of an object to be classified where there is provided processing means with an initial neural network, the processing means configured to determine a differential equation describing the initial neural network algorithm based on the neural network parameters, and to determine a solution to the differential equation in the form of a series expansion; and to convert the series expansion to a finite series expansion by limiting the number of terms in the series expansion to a finite number; and to determine the output classification in dependence on the finite series expansion.