Application of Feed-Forward Neural Networks for Classifying Acoustics Levels in Vehicle Cabin

摘要: Vehicle acoustical comfort and vibration in a passenger car cabin are the main factors that attract buyer purchase. Numerous studies have been carried out by automotive researchers to identify classify acoustics level vehicle cabin. The objective is form special benchmark for may be referred any improvement purpose. This study focused on sound quality change over engine speed [rp recognize noise pattern experienced Since it difficult express, evaluate or heard numerical scale, neural network optimization approach used levels into groups of annoyance levels. A feed forward technique applied classification algorithm, where can divided two phases: Learning Phase Classification Phase. developed model able scales which meaningful evaluation purposes.