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To achieve this, model predictive control (MPC) may be a suitable control technique.
To achieve this model, different Adaptive Neuro-Fuzzy Inference System (ANFIS) models (by changing the training optimization algorithms) are designed.
To achieve this model, we made use [4] of persistent identifiers such as the well-known DOI or digital object identifier for both the narrative and the data.
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Although the differentiation in a complete range of neuronal subtypes has not yet been achieved, this model system is extremely promising as it allows to study patient-derived cells which retain the original disease-causing mutation and can be differentiated in virtually all cell types.
To achieve this, two models, a Product Model and a Costgrammes Model, are presented and used to represent and capitalize technical knowledge.
To achieve this goal, model ANFIS was designed.
Three phases are carried out to achieve this objective: model identification, control design and evaluation, and implementation.
In this study it is taken into account, and in order to achieve this, a model of random received polarization based on Stokes parameters is given.
To achieve this, the model was initially run with likely parameter values and its output compared with data available in the literature and in stock-assessment reports.
To achieve this, the model suggests a structured approach that will allow for interaction between CSSS partners and community resources in order to provide integrated services.
To achieve this, the model was executed with the temperature-dependent parameters calculated from Equations 6 and 14 at temperatures of 155°C and 170°C.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com