Paper title: COMPARISON AMONG COMPUTATIONAL INTELLIGENCE METHODS FOR ENGINE KNOCK DETECTION. PART 1
Author(s): CLAUDIU OROS, CONSTANTIN RĂDOI, ADRIANA FLORESCU,
Abstract: The novelty this article brings is the use of neural networks and neuro-fuzzy modeling
to take the engine knock detection based on pressure and vibration samples taken from
an internal combustion engine to the next step from previous achievements, the ultimate
goal being higher detection rates and also faster response times than the classical non
neuro-fuzzy methods. Work started from the theoretical works available in the domain,
algorithms were adapted for the case in hand, then the study was led on how they would
be affected by the variety of situations that occur in internal-combustion engines with
the scope of real-time applications. Following the experiments, results were finally
compared showing significantly greater knock detection rate–time improvements than
other methods employed so far.
Keywords: Bayes classifier, Engine knock, Fuzzy Kwan-Cai neural-network,
Kohonen self-organizing map (SOM) Year: 2011 | Tome: 56 | Issue: 4 | Pp.: 418-427
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