Extending a model due to Derrida, Gardner, and Zippelius, we have studied the recognition ability of an extreme and asymmetrically diluted version of the Hopfield model for associative memory by including the effect of a stimulus in the dynamics of the system. We obtain exact results for the dynamic evolution of the average network superposition. The stimulus field was considered as proportional to the overlapping of the state of the system with a particular stimulated pattern. Two situations were analyzed, namely, the external stimulus acting on the initialization pattern (parallel stimulus) and the external stimulus acting on a pattern orthogonal to the initialization one (orthogonal stimulus). In both cases, we obtained the complete phase diagram in the parameter space composed of the stimulus field, thermal noise, and network capacity. Our results show that the system improves its recognition ability for parallel stimulus. For orthogonal stimulus two recognition phases emerge with the system locking at the initialization or stimulated pattern. We confront our analytical results with numerical simulations for the noiseless case T = 0.
This paper presents an analysis of speech signals based on quantification measures of recurrence plots. A comparison between healthy voices and voices affected by laryngeal pathologies (Reinke's edema, nodule and vocal fold paralysis) is made. In order to classify these signals as pathological or healthy, seven recurrence quantification measures are used: Determinism, maximum length of the diagonal structures, Shannon entropy of line distribution, slope of line of best fit, laminarity, length of longest vertical line segment and mean vertical line length or trapping time. Discriminant analysis methods (linear and quadratic) are applied to each feature individually, and to the vectors formed by feature combination with cross-validation classification rates up to 95.71±4.94% (95% confidence interval). Results show that the employed measures present a significant discriminative potential to distinguish healthy voices from pathological ones.
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