What are the factors affecting back propagation training?

These factors are as follows.

• Initial Weights. Weight initialization of the neural network to be trained contribute to the final solution.
• Cumulative weight adjustment vs Incremental Updating.
• The steepness of the activation function 𝜆
• Learning Constant 𝜂.
• Momentum method.

What is false regarding back propagation rule?

It is also called generalized delta rule. Error in output is propagated backwards only to determine weight updates. There is no feedback of signal at any stage. All of the mentioned. No, the answer is incorrect.

What are the factors that determine the convergence of the back propagation algorithm?

The factors that improve the convergence of Error Back Propotion Algorithm(EBPA) are called as learning factors they are as follows:

• Initial weights.
• Steepness of activation function.
• Learning constant.
• Momentum.
• Network architecture.
• Necessary number of hidden neurons.

What is true regarding back propagation learning rule?

What is true regarding backpropagation rule? Explanation: In backpropagation rule, actual output is determined by computing the outputs of units for each hidden layer. Explanation: The term generalized is used because delta rule could be extended to hidden layer units.

What are the main problem with the back-propagation learning algorithm?

Because each expert is only utilized for a few instances of inputs, back-propagation is slow and unreliable. And when new circumstances arise, the Mixture of Experts cannot adapt its parsing quickly. If a circumstance requires a new kind of expertise, existing Mixtures of Experts cannot add that specialization.

How can learning process be stopped in back-propagation rule?

The explanation is: If average gadient value fall below a preset threshold value, the process may be stopped.