Comparison Among Computational Intelligence Methods For Engine Knock Detection. Part 2
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.