Tracking and data association
Applied system identification
Linear System Theory and Design
Linear System Theory and Design
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Virtual fencing applications: Implementing and testing an automated cattle control system
Computers and Electronics in Agriculture
Robust classification of animal tracking data
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Review: Wireless sensors in agriculture and food industry-Recent development and future perspective
Computers and Electronics in Agriculture
Closed-loop identification revisited
Automatica (Journal of IFAC)
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Computers and Electronics in Agriculture
Developing WSN-based traceability system for recirculation aquaculture
Mathematical and Computer Modelling: An International Journal
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The identification of a mathematical model capable of describing the behaviour of animals given input such as feed has great potential for behavioural control purposes. Such models will allow to make predictions which are fundamental to any closed loop control such as control of the feeding. This paper investigates the problem of mathematically modelling animal behaviour. An observer Kalman filter identification method was successfully applied to input-output data and two models representing the hypotheses that animals are actively feeding and the hypotheses that animals are inactive were identified. The input and output of each of the identified models were feed dry matter offer and the pitch angle of the neck, respectively. The pitch angle of the neck of the animal was successfully measured and aggregated by a ZigBee-based wireless sensor network. Two fourth-order models describing the dynamics of an animal in the active and inactive behaviour modes showed good performance in terms of prediction error, cross-correlation between the residual and the output as well as cross-correlation between the residual and the input with 99% confidence interval. A multiple-model adaptive estimation approach was applied to determine the likelihood of each of the two models being the correct model for a specific input of dry matter feed. The average classification success rate was 87.2% for the whole experiment.