TY - GEN
T1 - Research on fish intelligence for fish trajectory prediction based on neural network
AU - Xue, Yanmin
AU - Liu, Hongzhao
AU - Zhang, Xiaohui
AU - Minami, Mamoru
PY - 2008
Y1 - 2008
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.
KW - Genetic algorithm
KW - Intelligent robot
KW - Neural network
KW - Predicting trajectory
KW - Visual servo
UR - http://www.scopus.com/inward/record.url?scp=59249087195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=59249087195&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-87732-5-41
DO - 10.1007/978-3-540-87732-5-41
M3 - Conference contribution
AN - SCOPUS:59249087195
SN - 3540877312
SN - 9783540877318
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 364
EP - 373
BT - Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
T2 - 5th International Symposium on Neural Networks, ISNN 2008
Y2 - 24 September 2008 through 28 September 2008
ER -