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RBF Neural Network Evolutionary Training

In this project, I implemented and trained a network to do regression, binary classification and multi class classification. I used evolutionary strategy for training.

In this implementation, the length of chromosome is not fixed, and the algorithm finds the optimum one(find the optimum number of bases).

List of contents:

Evolutionary Strategy Parameters

Representation

If your data is two dimensional:

<σ,x1,y1,r1,x2,y2,r2,...>

Initial Population

I select them randomly and uniformly.

Mutation

τ is changed by random normal distribution.

σ’ = σ • exp(τ • N(0,1))
x’i = xi + σ’ • N(0,1)

Note that τ α 1/ n½

Crossover

It just produce one child.

I assign Pc=0.4

Survivors selection

I use μ+λ and q-tournament methods. I take λ≈7μ and q=5.

Fitness

  • Regression:
e = 0.5 . Transpose(y-y*) . (y-y*)    fitness = 1/e
  • Binary classifier:
fitness = 1-sum(abs(sign(y)-y*))/2L
  • Multi classifier:
fitness = 1-sum(sign(abs(indexMax(y)-indexMax(y*)))))/L

I use one-hot encoding in multi classification.

Network Architecture

You can see the network architecture for regression below:

picture

You can see the network architecture for multi class classifier below:

picture

How to use

You can see how I used it in run.py.

Feed Data

You can feed xlsx data by read_excel method.

Some Results

The result of the dataset in dataset folder, is shown below:

  • Regression:

picture

error: 7%
  • Classification:

picture

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precision: 95%
  • Binary Classification:

picture

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precision: 98%