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First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation.
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Results of the tests indicate that the mean errors and variances of errors of the modelling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modelling the fluid dispensing.
In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described.
Inkjet gravimetry has become a useful method for controlled fluid dispensing.
A fluid dispensing system is one of the key processes to deliver fluid materials to various positions in assembly parts of several manufacturing industries.
Rules generated by the GA based knowledge discovery system have been validated using a computational system for process optimization of fluid dispensing.
Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach.
Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation.
We designed and evaluated the accuracy and usability of a device to regulate the volume of fluid dispensed during intravenous drip therapy.
Use of the device limits the volume of fluid dispensed during intravenous therapy and could potentially reduce the morbidity and mortality associated with overhydration in children receiving intravenous therapy.
The parameter identification of a fluid dispensing model by using with measurement values of the accumulated final volume is usually employed.
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