# A Precise and Simplified

# Approach to Neural Network

David Nguyen, Duy-Ky Nguyen, PhD

## 1. Neural Network Structure

A neuron does nothing unless the collective influence of all inputs reaches a threshold level. A nonzero-threshold neuron is computationally equivalent to a zero-threshold neuron with an extra link connected to an input that is always held at -1.

We have

or equivalently

Therefore, a neural network uses zero-threshold neurons with augmented input with -1.

where

(Eq 1)

(Eq 2)

We have an error between the actual output **Z** and the *desired* output **D**

(Eq 3)

we will find the weights **W** and

to eliminate the error. We have

(Eq 4)

if

(Eq 5)

then

since E > 0, E will reduce to zero.

## 2. Calculation of Error Gradient

In terms of components, by Eq.(1), (2) and (3), we have

(Eq 6)

(Eq 7)

(Eq 8)

By Eqs.(6) to (8) and from the chain rule, we have for a certain output node *k*

(Eq 9)

and for a certain hidden node *j*

or

(Eq 10)

where

So

(Eq 11)

(Eq 12)

## 3. Activating Functions

A step function is replaced by a differentiable sigmoid function. Typical activating functions are

- Logistic function
- Bipolar logistic function
- Hyperbolic Tangent function

## 4. Conclusion

The weights **W** and are arbitrarily initialized and updated by the following sequence

*forward*to compute the actual output*backward*to compute the error gradient

Then the weights are updated as follows

where l is a positive learning rate.

To calculate the error gradient, we have to start from the output to update the weight **W** and then go backward