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In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules.
The highest green and firing strength could be obtained in the samples with zircon flour and mullite bead, respectively.
The firing strength of the core prepared through the new process was improved because of the increase in the inorganic precursor that infiltrated the spaces/sites of the decomposed PVA, and it could be readily applied to the production of a core using 3D printing techniques without further shrinkage in a high-temperature heat treatment.
In the new conversion process, the formability of the core sample was controlled through the remaining organic compounds and the inorganic binder penetrating between particles and/or coated on the particle surface, and the firing strength was attributed to the glass phase generated by the inorganic precursor.
The normalized firing strength is equal to the ratio of the firing strength of a given rule to the sum of the firing strengths of all the rules.
Each node in this layer calculates normalized firing strength from the ratio of each node's firing strength to the sum of all rules' firing strengths.
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Until the firing strengths of all of the training patterns satisfy a pre-specified threshold, the process of rule generation is terminated.
The outputs of this layer are called normalized firing strengths, or normalized weights.
The results of this layer are referred to as normalized firing strengths.
According to [27], each data cluster can be considered as a crisp frame on which different types of MFs (and firing strengths) can be adapted.
This operation is also called normalization of firing strengths: O i 3 = w i ¯ = w i w 1 + w 2 ; i = 1, 2. (5).
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