Efficient Neighborhood Function and Learning Rate of Self-Organizing Map (SOM) for Cell Towers Traffic Clustering

The self-organizing map (SOM) neural network is based on unsupervised learning, and has found variety of applications. It is necessary to adjust the SOM parameters before starting learning process to ensure the best results. In this research, three types of data represent high and low traffic of specific cell tower with subscriber positions distribution in central of Iraq are investigated by self-organizing map (SOM). SOM functions and parameters influence its final results. Hence, several iteration of experiments are performed to test and analyze Bubble, Gaussian and Catgass neighborhood functions with three learning rates (linear, inverse of time and power series) and they were evaluated based on the quantization error. The experiments results show that Bubble function with linear learning rate gives the best result for clustering cell tower traffic.

Finite Element Formulation for the Vibration Analysis of Couple-Stress Continuum

This paper proposed a finite element formulation to analysis the vibration of couple-stress continuum. A four-node discrete couple-stress element relaxed the requirement of C1 continuity is developed. This element is modified by a bubble function, based on the classical four-ode Lagrange element. The element includes the internal bending constants and the internal initial moment of rotation. Numerical examples show that the present FE scheme is accurate for the eigenvalue analysis of couple-stress continuum structures, especially for the low order frequency analysis.