Self-organizing feature map with a momentum term

The objectives of this paper are to derive a momentum term in the Kohonen's self-organizing feature map algorithm theoretically and to show the effectiveness of the term by computer simulations. We will derive the self-organizing feature map algorithm having the momentum term through the following assumptions: (1) The cost function is E(n) = Σ(μ)(n)α(n-μ), where E(μ) is the modified Lyapunov function originally proposed by Ritter and Schulten at the μth learning time and Q is the momentum coefficient. (2) The latest weights are assumed in calculating the cost function E(n). According to our simulations, it has shown that the momentum term in the self-organizing feature map can considerably contribute to the acceleration of the convergence.