Soft-computing method for establishing the heat dissipation law in a diesel common rail engine
First Claim
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1. 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, wherein the system set-up comprises the following steps:
- choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed;
applying the Transform Ψ
to said signal;
realizing a corresponding neural network MLP by means of an evolutive algorithm;
training and testing said neural network MLP.
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Abstract
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.
13 Citations
20 Claims
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1. 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, wherein the system set-up comprises the following steps:
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choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying the Transform Ψ
to said signal;realizing a corresponding neural network MLP by means of an evolutive algorithm; training and testing said neural network MLP. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for modeling a parameter of an engine having an operating cycle, the method comprising:
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selecting a first number of first functions of a first variable that together represent the values of the parameter over a portion of the operating cycle; transforming the selected first functions into a second number of second functions of a second variable, each of the second functions having a corresponding coefficient; forming a neural network by applying an evolutive algorithm to the second functions; and training the neural network by determining values for the coefficients. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A vehicle, comprising:
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an engine having a first operating parameter that is dependent on a control parameter; a controller coupled to the engine and operable to, receive a value of the first operating parameter, generate a value of the control parameter in response to the received value of the first operating parameter, and provide the generated value of the control parameter to the engine; and a neural network coupled to the controller and operable to, receive the generated value of the control parameter from the controller, generate the value of the first operating parameter in response to the received value of the control parameter, and provide the value of the first operating parameter to the controller. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification