Application and evaluation of the neural network in gearbox

Abstract

We developed old designed of a Back-Propagation neural network (BPNN), which it was designed by other researchers, and we made modification in their structure. The 1 st velocity ratio was discriminated by lowest speed, and highest twist. The 6 th velocity ratio was discriminated by highest speed, and lowest twist. The aim of this paper is to design neural structure get best performance to control an electrical automotive transportation six-speed gearbox of the vehicle. We focus on the evaluation of the BPNN to select the suitable number of layers and neurons. Experimentally, the structure of the proposed BPNN are constructed from four layers: eight input nodes in the first layer that received data in binary number, 45 neurons in 1 st hidden-layer, 25 neurons in 2 nd hidden-layer, and 6 neurons in the fourth layer. The MSE and number of Epochs are the main factors used for the evaluation of the proposed structure, and compared with the other structures which was designed by other researchers. Experimentally, we discovered that the best value of Epoch and MSE was chosen when the BPNN consisted of two hidden-layers, 45, and 25 neurons in the 1 st and 2 nd hidden-layer respectively. The implementation was applied using MATLAB software.

A smart method for spark using neural network for big data

“…Softmax is a type of logistic function in mathematics. The softmax function accommodates outputs of each unit in between 0 to 1, displayed in a K-dimensional vector of random real numbers [29][30][31]. The function is used as an activation function due to its categorical probability distribution characteristic.…”