Comparison of BP and GRNN Algorithm for Factory Monitoring

Abstract

Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

Applications of General Regression Neural Networks in Dynamic Systems

“…In the second case an adaptation algorithm is required to adjust GRNN parameters. The second approach is shown in Fig Some of the applications of GRNN in control systems include dead-zone estimation and compensation in motion control of a traveling wave ultrasonic motor [15], fault diagnosis of power system [16], intelligent battery charger [17], microgrid hybrid power systems control [18], bipedal standing stabilization [19], air conditioning control [20], wind generation system [21], helicopter motion control [22], active vibration control [23], active noise cancellation [24], rat-like robot control [25], pipe climbing robot control [26], tracking-control for an optomechatronical Image derotator [27], tracking in marine navigational radars [28], factory monitoring [29], and flapping wing micro aerial vehicle control [3].…”

Vehicle Sideslip Angle Estimation Based on General Regression Neural Network

“…It also has a stronger ability of parameter approximation and classification than BP neural network. GRNN is a special form of radial basis function neural network [10,11]. Its abilities of approximate function and learning are very strong.…”