Here are some information about parameters used in the settings. Found on Wikipedia.
Network Parameters - Wikipedia
Network Parameters
There are a number of different parameters that must be decided upon when designing a neural network. Among these parameters are the number of layers, the number of neurons per layer, the number of training iterations, et cetera. Some of the more important parameters in terms of training and network capacity are the number of hidden neurons, the learning rate and the momentum parameter.
Number of neurons in the hidden layer
Hidden neurons are the neurons that are neither in the input layer nor the output layer. These neurons are essentially hidden from view, and their number and organization can typically be treated as a black box to people who are interfacing with the system. Using additional layers of hidden neurons enables greater processing power and system flexibility. This additional flexibility comes at the cost of additional complexity in the training algorithm. Having too many hidden neurons is analogous to a system of equations with more equations than there are free variables: the system is over specified, and is incapable of generalization. Having too few hidden neurons, conversely, can prevent the system from properly fitting the input data, and reduces the robustness of the system.
Data type: Integer Domain: [1, ∞) Typical value: 8
Meaning: Number of neurons in the hidden layer (additional layer to the input and output layers, not connected externally).
Learning Rate
Data type: Real Domain: [0, 1] Typical value: 0.3
Meaning: Learning Rate. Training parameter that controls the size of weight and bias changes in learning of the training algorithm.
Momentum
Data type: Real Domain: [0, 1] Typical value: 0.9
Meaning: Momentum simply adds a fraction m of the previous weight update to the current one. The momentum parameter is used to prevent the system from converging to a local minimum or saddle point. A high momentum parameter can also help to increase the speed of convergence of the system. However, setting the momentum parameter too high can create a risk of overshooting the minimum, which can cause the system to become unstable. A momentum coefficient that is too low cannot reliably avoid local minima, and can also slow down the training of the system.
Training type
Data type: Integer Domain: [0, 1] Typical value: 1
Meaning: 0 = train by epoch, 1 = train by minimum error
Epoch
Data type: Integer Domain: [1, ∞) Typical value: 5000000
Meaning: Determines when training will stop once the number of iterations exceeds epochs. When training by minimum error, this represents the maximum number of iterations.
Minimum Error
Data type: Real Domain: [0, 0.5] Typical value: 0.01
Meaning: Minimum mean square error of the epoch. Square root of the sum of squared differences between the network targets and actual outputs divided by number of patterns (only for training by minimum error).