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In this study, performance and emission characteristics of an internal combustion (IC) diesel engine and petrol-driven engine were modeled by Artificial Neural Network (ANN). Diesel engine input parameters are air flow rate (Aflr), boost pressure (Pb), fuel rate (Frt), cycle (Cy) and load (L) whereas input parameters of the petrol-driven engine are advance(More)
The paper includes reverse modeling of a diesel engine performance and emission characteristics. Modeling is done by fuzzy clustering method (FCM) and Adaptive Neural Fuzzy Inference System (ANFIS). Firstly, outputs and inputs parameters of a diesel engine were replaced as part of system. Later, these parameters were grouped into optimal numbers(More)
In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NO<sub>x</sub>, power (P), torque (Tq) and air-flow rate (Afr) were(More)
Comparision of numerical tehnique and Al techniques for determination of performance and emission characteristics of a diesel engine has been done in this study. Three different techniques namely <b>multiple regression analysis, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)</b> were used for modeling aims. Engine torque(More)
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