Cell Status Diagnosis for the Aluminum Production on BP Neural Network with Genetic Algorithm - Advanced Research on Computer Education, Simulation and Modeling - page 149

network has three layers: input layer ( I ), hidden layer ( H ) and output layer ( O ). Among
them I i is the output of ith node at input layer, H j is the output of jth node at hidden layer,
O k is the output of kth node at output layer. WIH ij is the weight between the ith node of
input layer and the jth node of hidden layer, WHO jk is the weight between the jth node
of hidden layer and the kth node of output layer, h j is the threshold value of jth node at
hidden layer, O k is the threshold value of kth node at output layer. Where 1

≤≤ i 1 0

1 ≤≤

≤≤ .
The structure of BP neural network is shown in the Figure 2 .

8

1 3

Fig. 2. BP neural network

4 Optimizing the BP Neural Network by Genetic Algorithm

4.1 Optimization

1. Parameters initialization. Setting some parameters: initial population G is 30,
maximum genetic algebra T is 100, crossover rate P crossover is 0.6, mutation rate P mutation
is 0.09. The fitness function f i is realized by the software MATLAB.
2. Coding and generating initial population. We code any weight and threshold value
with real. Then construct a code chain. Every chain is a collection of weight and
threshold value in the BP neural network. Finally an initial population which contains
30 individuals is generated randomly.
3. Computing the Adapter value of every individual according to the fitness
function. Judging whether it meets the requirements or not. If not, conducting the
genetic operation and comes out the new individuals. Then we compute the sun of error
squares of the artificial neural network. If the result does not reach the expectation,
where the expectation value ε GA is 5.0, the genetic operation went on. If it satisfies
certain condition within 100 times operation, we will finally get the optimal solution.
4. Decomposing the optimal solution into the weight and threshold value of BP neural
network.

The flowchart of genetic algorithm is shown in Figure 3.
We use genetic algorithm to optimize neural network weight and threshold value.
Then conduct the neural network learning with BP algorithm. The software MATLAB
is used for programming in the design. After 2571 times training, the neural network
will reach the expected precision, where expected precision ε BP is 0.005.