Research on fish intelligence for fish trajectory prediction based on neural network

Yanmin Xue; Hongzhao Liu; Xiaohui Zhang; Mamoru Minami

doi:10.1007/978-3-540-87732-5_41

Research on fish intelligence for fish trajectory prediction based on neural network

Yanmin Xue, Hongzhao Liu, Xiaohui Zhang, Mamoru Minami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept trying to escape from the net attached at robot's hand were studied through lots of experiments. The patterns were divided into five sorts and the learning procedures were divided into three stages. Based on this, the position, orientation and speed of each time were used as the input of multi layer perceptron (MLP) neural networks (NN), and the positions of the fish at next time were the outputs. The NN adopted extended delta-bar-delta (DBD) algorithm as learning method. Thus the NNs were constructed to study the moving regulations of fish in every pattern to predict the moving trajectory. The simulation results shows that the BP NN constructed here have the advantage of faster learning rate, higher identifying precision and can predict the fish trajectory successfully. The research is significant for visual servo in robotic system.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
Publisher	Springer Verlag
Pages	364-373
Number of pages	10
Edition	PART 1
ISBN (Print)	3540877312, 9783540877318
DOIs	https://doi.org/10.1007/978-3-540-87732-5_41
Publication status	Published - 2008
Externally published	Yes
Event	5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China Duration: Sep 24 2008 → Sep 28 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th International Symposium on Neural Networks, ISNN 2008
Country/Territory	China
City	Beijing
Period	9/24/08 → 9/28/08

Keywords

Genetic algorithm
Intelligent robot
Neural network
Predicting trajectory
Visual servo

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-87732-5_41

Cite this

Xue, Y., Liu, H., Zhang, X., & Minami, M. (2008). Research on fish intelligence for fish trajectory prediction based on neural network. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings (PART 1 ed., pp. 364-373). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_41

Research on fish intelligence for fish trajectory prediction based on neural network. / Xue, Yanmin; Liu, Hongzhao; Zhang, Xiaohui et al.

Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. p. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xue, Y, Liu, H, Zhang, X & Minami, M 2008, Research on fish intelligence for fish trajectory prediction based on neural network. in Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5263 LNCS, Springer Verlag, pp. 364-373, 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, 9/24/08. https://doi.org/10.1007/978-3-540-87732-5_41

Xue Y, Liu H, Zhang X, Minami M. Research on fish intelligence for fish trajectory prediction based on neural network. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1 ed. Springer Verlag. 2008. p. 364-373. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-87732-5_41

Xue, Yanmin ; Liu, Hongzhao ; Zhang, Xiaohui et al. / Research on fish intelligence for fish trajectory prediction based on neural network. Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. pp. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Research on fish intelligence for fish trajectory prediction based on neural network",

abstract = "This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept trying to escape from the net attached at robot's hand were studied through lots of experiments. The patterns were divided into five sorts and the learning procedures were divided into three stages. Based on this, the position, orientation and speed of each time were used as the input of multi layer perceptron (MLP) neural networks (NN), and the positions of the fish at next time were the outputs. The NN adopted extended delta-bar-delta (DBD) algorithm as learning method. Thus the NNs were constructed to study the moving regulations of fish in every pattern to predict the moving trajectory. The simulation results shows that the BP NN constructed here have the advantage of faster learning rate, higher identifying precision and can predict the fish trajectory successfully. The research is significant for visual servo in robotic system.",

keywords = "Genetic algorithm, Intelligent robot, Neural network, Predicting trajectory, Visual servo",

author = "Yanmin Xue and Hongzhao Liu and Xiaohui Zhang and Mamoru Minami",

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N2 - This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept trying to escape from the net attached at robot's hand were studied through lots of experiments. The patterns were divided into five sorts and the learning procedures were divided into three stages. Based on this, the position, orientation and speed of each time were used as the input of multi layer perceptron (MLP) neural networks (NN), and the positions of the fish at next time were the outputs. The NN adopted extended delta-bar-delta (DBD) algorithm as learning method. Thus the NNs were constructed to study the moving regulations of fish in every pattern to predict the moving trajectory. The simulation results shows that the BP NN constructed here have the advantage of faster learning rate, higher identifying precision and can predict the fish trajectory successfully. The research is significant for visual servo in robotic system.

AB - This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept trying to escape from the net attached at robot's hand were studied through lots of experiments. The patterns were divided into five sorts and the learning procedures were divided into three stages. Based on this, the position, orientation and speed of each time were used as the input of multi layer perceptron (MLP) neural networks (NN), and the positions of the fish at next time were the outputs. The NN adopted extended delta-bar-delta (DBD) algorithm as learning method. Thus the NNs were constructed to study the moving regulations of fish in every pattern to predict the moving trajectory. The simulation results shows that the BP NN constructed here have the advantage of faster learning rate, higher identifying precision and can predict the fish trajectory successfully. The research is significant for visual servo in robotic system.

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KW - Intelligent robot

KW - Neural network

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KW - Visual servo

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Research on fish intelligence for fish trajectory prediction based on neural network

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