@article {408,
	title = {Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors},
	journal = {Spanish Journal of Agricultural Research},
	volume = {9},
	year = {2011},
	pages = {377-388},
	abstract = {<p>This paper evaluates the use of low-cost global positioning system (GPS) receivers in the autonomous guidance of agricultural tractors. An autonomous guidance system was installed in a 6400 John Deere agricultural tractor. A lowcost GPS receiver was used as positioning sensor. Three different control laws were implemented in order to evaluate the autonomous guidance of the tractor with the low-cost receiver. The guidance was experimentally tested with the tracking of straight trajectories and with the step response. The total guidance error was obtained from the receiver accuracy and from the guidance error. For the evaluation of the receiver{\textquoteright}s accuracy, positioning data from several lowcost receivers were recorded and analyzed. For the evaluation of the guidance error, tests were performed with each control law at three different speeds. The conclusions obtained were that relative accuracy of low-cost receivers decreases with the time; that for an interval lower than 15 min, the error usually remains below 1 m; that all the control laws have a similar behavior and it is conditioned by the control law adjustment; that automatic guidance with lowcost receivers is possible with speeds that went up to 9 km h -1; and finally, that the total error in the guidance is mainly determined by the receiver{\textquoteright}s accuracy.</p>
},
	issn = {1695971X},
	doi = {https://doi.org/10.5424/sjar/20110902-088-10},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79959669468\&partnerID=40\&md5=774d42717ec127c9a6c5e25864da9722},
	author = {Sergio Alonso-Garcia and Jaime Gomez-Gil and J I Arribas}
}
@inbook {421,
	title = {Estimation of Posterior Probabilities with Neural Networks: Application to Microcalcification Detection in Breast Cancer Diagnosis},
	booktitle = {Handbook of Neural Engineering},
	year = {2006},
	pages = {41-58},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	isbn = {9780470056691},
	doi = {10.1002/9780470068298.ch3},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-42249107409\&partnerID=40\&md5=aac6237961cec1a48c0e843a9a1912a4},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and Carlos Alberola-Lopez}
}
@inbook {arribas2005estimation,
	title = {Estimation of Posterior Probabilities with Neural Networks: Application to Microcalcification Detection in Breast Cancer Diagnosis},
	booktitle = {Handbook of Neural Engineering},
	year = {2005},
	pages = {41{\textendash}58},
	publisher = {Wiley Online Library},
	organization = {Wiley Online Library},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and Carlos Alberola-Lopez}
}
@conference {412,
	title = {Estimates of constrained multi-class a posteriori probabilities in time series problems with neural networks},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks},
	year = {1999},
	publisher = {IEEE, United States},
	organization = {IEEE, United States},
	address = {Washington, DC, USA},
	abstract = {<p>In time series problems, where time ordering is a crucial issue, the use of Partial Likelihood Estimation (PLE) represents a specially suitable method for the estimation of parameters in the model. We propose a new general supervised neural network algorithm, Joint Network and Data Density Estimation (JNDDE), that employs PLE to approximate conditional probability density functions for multi-class classification problems. The logistic regression analysis is generalized to multiple class problems with softmax regression neural network used to model the a-posteriori probabilities such that they are approximated by the network outputs. Constraints to the network architecture, as well as to the model of data, are imposed, resulting in both a flexible network architecture and distribution modeling. We consider application of JNDDE to channel equalization and present simulation results.</p>
},
	keywords = {Approximation theory, Computer simulation, Constraint theory, Data structures, Joint network-data density estimation (JNDDE), Mathematical models, Multi-class a posteriori probabilities, Neural networks, Partial likelihood estimation (PLE), Probability density function, Regression analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0033325263\&partnerID=40\&md5=8c6134020b0b2a9c5ab05b131c070b88},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and T Adali and H Ni and B Wang and A R Figueiras-Vidal}
}
