Abstract:

The silk moth (Bombyx mori) exhibits efficient Chemical Plume Tracing (CPT), which is ideal for biomimetics. However, there is insufficient quantitative understanding of its CPT behavior. We propose a hierarchical classification method to segment its natural CPT locomotion and to build its inverse model for detecting stimulus input. This provides the basis for quantitative analysis. The Gaussian mixture model with expectation–maximization algorithm is used first for unsupervised classification to decompose CPT locomotion data into Gaussian density components that represent a set of quantified elemental motions. A heuristic behavioral rule is used to categorize these components to eliminate components that are descriptive of the same motion. Then, the echo state network is used for supervised classification to evaluate segmented elemental motions and to compare CPT locomotion among different moths. In this case, categorized elemental motions are used as the training data to estimate stimulus time. We successfully built the inverse CPT behavioral model of the silk moth to detect stimulus input with good accuracy. The quantitative analysis indicates that silk moths exhibit behavioral singularity and time dependency in their CPT locomotion, which is dominated by its singularity.

Key words: biomimetics, recognition, learning and adaptive systems, chemical plume tracing, quantitative analysis