RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

Mixing of 1/2^- Octets under SU(3) Symmetry

We investigate the J^p=1/2^- baryons in the octets based on flavor SU(3) symmetry. Since baryons with same quantum numbers can mix with each other, we consider the mixing between two octets before their mixing with the singlet. Most predicted decay widths are consistent with the experimental data, and meanwhile we predict two possible $\Xi$ mass ranges of the two octets.Comment: 8 latex page

A New Method to Calculate Electromagnetic Impedance Matching Degree in One-Layer Microwave Absorbers

A delta-function method was proposed to quantitatively evaluate the electromagnetic impedance matching degree. Measured electromagnetic parameters of {\alpha}-Fe/Fe3B/Y2O3 nanocomposites are applied to calculate the matching degree by the method. Compared with reflection loss and quarter-wave principle theory, the method accurately reveals the intrinsic mechanism of microwave transmission and reflection properties. A possible honeycomb structure with promising high-performance microwave absorption according to the method is also proposed.Comment: 13 pages, 3 figure