摘要:
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, includes the following steps: choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying a Transform Ψ to the dissipation speed signal (HRR) of the heat; carrying out analysis of homogeneity of the Transform Ψ output; realizing a corresponding neural network MLP wherein the design is guided by an evolutive algorithm; and training and testing the neural network MLP.
摘要:
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, includes the following steps: choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying a Transform Ψ to the dissipation speed signal (HRR) of the heat; carrying out analysis of homogeneity of the Transform Ψ output; realizing a corresponding neural network MLP wherein the design is guided by an evolutive algorithm; and training and testing the neural network MLP.
摘要:
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, includes the following steps: choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying a Transform Ψ to the dissipation speed signal (HRR) of the heat; carrying out analysis of homogeneity of the Transform Ψ output; realizing a corresponding neural network MLP wherein the design is guided by an evolutive algorithm; and training and testing the neural network MLP.
摘要:
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, includes the following steps: choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying a Transform Ψ to the dissipation speed signal (HRR) of the heat; carrying out analysis of homogeneity of the Transform Ψ output; realizing a corresponding neural network MLP wherein the design is guided by an evolutive algorithm; and training and testing the neural network MLP.