## 2pNS12. Estimation of equivalent sound level by invoking a neural net.

### Session: Tuesday Afternoon, December 3

### Time: 5:15

**Author: Kanenori Imai**

**Location: Faculty of Medical Eng., Suzuka Univ. of Medical Sci. and Technol., 1001-1 Kishioka, Suzuka, Mie, 510-02 Japan**

**Author: Akinori Hayashi**

**Location: Faculty of Medical Eng., Suzuka Univ. of Medical Sci. and Technol., 1001-1 Kishioka, Suzuka, Mie, 510-02 Japan**

**Author: Kazuhiro Kuno**

**Location: Mie Univ., Mie, 514 Japan**

**Author: Kazuo Ikegaya**

**Location: Nagoya Univ., Nagoya, Japan**

**Abstract:**

The equivalent sound level L[inf eq] for road traffic noise is estimated
using two models. One is a nonlinear neural model in which the transformation
sometimes becomes analytical when it is expanded to a series. Therefore, the
output function of the net is examined, and a means of partitioning the data for
learning and evaluation is described. The other model is a prediction equation
in which the decay due to distance is added to the unit pattern based on an
expression derived from Poisson traffic flow. The variables included in the
equation are then selected as inputs to the net, and learning is evaluated. The
output of the net is compared with the results of the prediction equation. The
neural net learns functional relations with relatively little polarization, and
predictions are easily made since the data are partitioned. The degree of
prediction errors is approximately the same for both the equation and the neural
net.

ASA 132nd meeting - Hawaii, December 1996