(Brest State Technical University 2009 Spring Semester: Course Practice)

Recurrent Neural Network

Akira Imada (e-mail akira@bstu.by)

This document is still under construction and was lastly modified on May 9, 2009

1 Model and Method

1.1 Continuous Neuron

Renew of states

x_{i}(t + 1) = f(∑_{j=1}^{N} w_{ij}(t)x_{j}(t) + I_{i}) (i = 1, 2, 3 ⋯, N)

Transfer function – Sigmoid

f(x) = \frac{1 + tanh(Gx)}{2}

Update of weights w_{ij}(t + 1) = w_{ij}(t) + ηx_{i}(t + 1)x_{j}(t) (where η = 0.01 here, for example.)

(Practice – Neural Networks) 3

1.2 McCulloh-Pits Neuron

Renew of states

s_{i}(t + 1) = sgn(∑_{j=0}^{99} w_{ij}s_{j}(t))

Transfer function