Self validating model
This study proposes a self-validation cerebellar model articulation controller (SVCMAC) neural network which can yield high accuracy of predication and low false-negative rate for breast cancer diagnosis.With its self-validation unit, the SVCMAC neural network has higher classification accuracy than the conventional CMAC neural network.It doesn’t work yet, but this is what we’d want our validator to deal with in the end.Instead of assigning values directly to a variable, we decide every property is an object with at least two sub-properties: its actual value, and an array of its validation rules.The parameters of the receptive-field basis function and the weights are all updated first by training data, and the most suitable parameters are then chosen through the self-validation algorithm to retrain the neural network for better performance.
In programming, a service is a unit that externalizes business logic from entities.
Instead, we’ll create a reusable unit we can rely on to build the foundations of our program. Until now, when we wanted to validate data before setting it, our model’s setter methods looked something like this: Gross.
Imagine if we wanted to set several values, like all the properties of a single object, in the same method?
This means we’ll need to do it in every new setter method, which is repetitive and not as legible as it could be.
Let’s try to think about a method that would do it all for us: check if the validation passes, and set the data if it does.