Extreme Learning Machine towards Dynamic Model Hypothesis in Fish Ethology Research

Abstract

In This Paper, We Present One Dynamic Model Hypothesis to Perform Fish Trajectory Tracking in the Fish Ethology Research and Develop the Relevant Mathematical Criterion on the Basis of the Extreme Learning Machine (Elm). It is Shown that the Proposed Scheme Can Conduct the Non-Linear and Non Gaussian Tracking Process by Multiple Historical Cues and Current Predictions - the State Vector Motion, the Color Distribution and the Appearance Recognition, All of Which Can Be Extracted from the Single-Hidden Layer Feedforward Neural Network (Slfn) at Diverse Levels with Elm. the Strategy of the Hierarchical Hybrid Elm Ensemble Then Combines the Individual Slfn of the Tracking Cues for the Performance Improvements. the Simulation Results Have Shown the Excellent Performance in Both Robustness and Accuracy of the Developed Approach. © 2013 Elsevier B.v.

Department(s)

Engineering Management and Systems Engineering

Comments

National Natural Science Foundation of China, Grant 2012BAD28B05

Keywords and Phrases

Color distribution; Dynamic state space; Extreme learning machine; Fish ethology; Object recognition; Video surveillance system

International Standard Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

27 Mar 2014

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