By designing a proper electromagnetic constraint within a memristor, and introducing it into a dynamical system with the increase of dimension, a class of memristive chaotic systems with 2-D offset boosters is constructed. One offset constant directly applies offset boosting of a system variable while the other modifies the offset of two variables simultaneously. Furthermore, when the derived structure satisfies a specific feedback balance law, the property of total amplitude control can be obtained. The circuit implementation is consequently realized and the results are in line with the theoretical analysis.

This paper reports a simple nonlinear approach to online acoustic speech pathology detection for automatic screening purposes. Straightforward linear preprocessing followed by two nonlinear measures, based parsimoniously upon the biophysics of speech production, combined with subsequent linear classification, achieves an overall normal/pathological detection performance of 91.4%, and over 99% with rejection of 15% ambiguous cases. This compares favourably with more complex, computationally intensive methods based on a large number of linear and other measures. This demonstrates that nonlinear approaches to speech pathology detection, informed by biophysics, can be both simple and robust, and are amenable to implementation as online algorithms