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As mentioned above, a US risk assessment rule r consists of an antecedent part Cond and a consequent part Class.
When there is a strong comorbidity between two conditions, for example hypertension and diabetes, we can have a consequent part of the patients presenting the two conditions and the two treatments.
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In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model.
In the first place, DMELA encodes an antecedent part of a TSK-type fuzzy rule into a chromosome and utilizes a RLS to estimate the consequent part of a TSK-type fuzzy rule.
Since the coding step only decides an antecedent part of a fuzzy rule, the consequent part is undetermined.
Each rule in this FLC has an SMC or an SFC in the consequent part.
A TNFN [25] employs a linear combination of the crisp inputs as the consequent part of a fuzzy rule.
In the proposed approach, the SRWNN is employed to construct a full-adaptive self-recurrent consequent part for each fuzzy rule of a TSK fuzzy model.
The training algorithm consists of three steps: partitioning of the input output space using a fuzzy clustering method; determination of parameters of the consequent part of a rule from over-determined batch least-squares (LS) formulation of the problem, using singular value decomposition algorithm; and adaptation of these parameters using recursive least-squares method.
The crisp binary values of either ON or OFF allow the pediatricians to assume that if the condition given in the antecedent part of the rule is true, then there would be an implication on the consequent part of the rule.
The consequent part of a fuzzy rule used in DMELA is estimated by an RLS approach.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com