Information Reduction for Chaotic Patterns

Chaotic Itinerancy for Patterns Separation

Chaotic neural network with external inputs has been used as a mixed input pattern separator. In contrast to previous work on the subject, highly chaotic dynamical system (LLE ≈ 0.6) is applied here. The network is based on a “dynamical mapping” scheme as an effective framework for cortical mapping. This feature allows for a more effective pattern retrieval and separation by the network.

Model reduction methods for chaotic systems

Computation with chaotic patterns

The paper is focused on how chaotic patterns, occurring in nature, might be used by biological 7 organisms to perform computations. This issue is investigated in the context of neural systems. As a simple model of chaotic patterns, the world of Sierpinski triangles is analysed. The paper 9 introduces the Sierpinski basis functions and the Sierpinski brain, which is able to perform classi)cation...

M-ary Chaotic Sequence Based SLM-OFDM System for PAPR Reduction without Side-Information

Selected Mapping (SLM) is a PAPR reduction technique, which converts the OFDM signal into several independent signals by multiplication with the phase sequence set and transmits one of the signals with lowest PAPR. But it requires the index of the selected signal i.e. side information (SI) to be transmitted with each OFDM symbol. The PAPR reduction capability of the SLM scheme depends on the se...

Chaotic Patterns in Aeroelastic Signals

This work presents the analysis of nonlinear aeroelastic time series from wing vibrations due to airflow separation during wind tunnel experiments. Surrogate data method is used to justify the application of nonlinear time series analysis to the aeroelastic system, after rejecting the chance for nonstationarity. The singular value decomposition SVD approach is used to reconstruct the state spac...