There exists a neural network model designed to predict a specific output, detailed in a published article. The model comprises 14 inputs, each normalized with minimum and maximum parameters specified for normalization. It incorporates six hidden layers, with the article providing the neural network's weight parameters from the input to the hidden layers, along with biases. Similarly, the parameters from the output layer to the hidden layers, including biases, are also documented.
The primary inquiry revolves around extracting the mathematical equation suitable for implementation in Excel or Python to facilitate output prediction.
Neural networks are too complex for a direct mathematical equation. Techniques like sensitivity analysis can help understand how they work. For simple models, an equation might exist, but for complex ones, it's not practical. Consider interpretable models or using the network directly for predictions if an equation isn't crucial.
Due to the number of parameters, NNs are rarely explicitly expressed as mathematical equations. However, it is possible, and the process is relatively straightforward.
1. Look up the equation for each component (activation/convolution/etc) - the libraries like pytorch have them in the docs.
2. Create the resulting equation by combining all components, typically using the functional composition.
I'd recommend using the lambda calculus notation, it results in a much more readable and concise equation.
To extract mathematical equations from an Artificial Neural Network (ANN), you can use a technique called sensitivity analysis. Sensitivity analysis involves calculating the sensitivity of the output of the network with respect to changes in the input.
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