### Abstract

The prediction of the trajectory of mobile objects is important in many robotics applications like robot motion planning and collision avoidance. In most cases, the measurements, on which predictions are based, are subject to noise and errors. This paper presents a neural network based approach for the prediction of short-term motions of mobile objects. We studied the effect of white additive noise and Gaussian noise on the prediction accuracy. An adaptive continued learning strategy is used to reduce the prediction error and accurately track the mobile objects. An empirical study was conducted to determine the architectural features of the network (number of layers and number of neurons in each layer) and the learning parameters (learning rate, momentum factor and convergence criterion) that minimize the mean squared prediction error giving an acceptable time response. The mean squared error, and the average time performance of the network (number of learning steps before convergence) are used as performance criteria. The network results are compared with those obtained from a linear regression algorithm. The neural network outperformed the linear regression in accurately predicting swiftly changing motion patterns.

Original language | English (US) |
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Pages (from-to) | 183-189 |

Number of pages | 7 |

Journal | Proceedings of SPIE - The International Society for Optical Engineering |

Volume | 1965 |

DOIs | |

State | Published - Sep 2 1993 |

Event | Applications of Artificial Neural Networks IV 1993 - Orlando, United States Duration: Apr 11 1993 → Apr 16 1993 |

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### ASJC Scopus subject areas

- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering

### Cite this

**Neural network prediction of short-term motions of mobile objects in noisy environments.** / Tawfik, Ahmed Y.; Zhu, Qiuming.

Research output: Contribution to journal › Conference article

}

TY - JOUR

T1 - Neural network prediction of short-term motions of mobile objects in noisy environments

AU - Tawfik, Ahmed Y.

AU - Zhu, Qiuming

PY - 1993/9/2

Y1 - 1993/9/2

N2 - The prediction of the trajectory of mobile objects is important in many robotics applications like robot motion planning and collision avoidance. In most cases, the measurements, on which predictions are based, are subject to noise and errors. This paper presents a neural network based approach for the prediction of short-term motions of mobile objects. We studied the effect of white additive noise and Gaussian noise on the prediction accuracy. An adaptive continued learning strategy is used to reduce the prediction error and accurately track the mobile objects. An empirical study was conducted to determine the architectural features of the network (number of layers and number of neurons in each layer) and the learning parameters (learning rate, momentum factor and convergence criterion) that minimize the mean squared prediction error giving an acceptable time response. The mean squared error, and the average time performance of the network (number of learning steps before convergence) are used as performance criteria. The network results are compared with those obtained from a linear regression algorithm. The neural network outperformed the linear regression in accurately predicting swiftly changing motion patterns.

AB - The prediction of the trajectory of mobile objects is important in many robotics applications like robot motion planning and collision avoidance. In most cases, the measurements, on which predictions are based, are subject to noise and errors. This paper presents a neural network based approach for the prediction of short-term motions of mobile objects. We studied the effect of white additive noise and Gaussian noise on the prediction accuracy. An adaptive continued learning strategy is used to reduce the prediction error and accurately track the mobile objects. An empirical study was conducted to determine the architectural features of the network (number of layers and number of neurons in each layer) and the learning parameters (learning rate, momentum factor and convergence criterion) that minimize the mean squared prediction error giving an acceptable time response. The mean squared error, and the average time performance of the network (number of learning steps before convergence) are used as performance criteria. The network results are compared with those obtained from a linear regression algorithm. The neural network outperformed the linear regression in accurately predicting swiftly changing motion patterns.

UR - http://www.scopus.com/inward/record.url?scp=84910925917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84910925917&partnerID=8YFLogxK

U2 - 10.1117/12.152575

DO - 10.1117/12.152575

M3 - Conference article

AN - SCOPUS:84910925917

VL - 1965

SP - 183

EP - 189

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

SN - 0277-786X

ER -