DESIGN OF A PROPOSED NEURAL NETWORK FOR SOUND QUALITY ANALYSIS OF DIFFERENT TYPES FOR CAR SYSTEMS.

Nowadays, in spite of advanced technology, there are still some sound problems on modern cars because of mechanical parts, oil lubrications, and electric motors. Due to these unwanted problems, it is necessary to design intelligent predictors such as artificial neural networks. In this investigation, a procedure of testing and evaluation on the sound quality of two types of cars are proposed and sound quality is analyzed through the cars road running test on the providing ground, which is carried out with varying running speed. To improve and predict the results of experimental approach analysis, a proposed neural network predictor is also designed to model of the system for possible experimental applications. The proposed neural network is a feedforward type network, which consists of multi hidden layers. Three different training algorithms are used for training the network. As basic factors for sound quality, only objective factors a considered such as loudness, sharpness, speech intelligibility, sound pressure level. The correlation between sound pressure level and other factors are discussed from a point of view of running speed dependency. Results of both computer simulations and experiments show that the neural predictor algorithm gives good results at accommodating different cases and provides superior prediction on two cars's sound analysis. [ABSTRACT FROM AUTHOR]