Over training is akin to the issue of overfitting data. There exists an overtraining issue in the design of NN training process. This is very important while the neural network is trained to get very small errors which may not respond properly in wind speed prediction.
One of the major problems facing researchers is the selection of hidden neurons using neural networks (NN). The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks. The survey has been made for the fixation of hidden neurons in neural networks. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. The results show that proposed model improves the accuracy and minimal error. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. This paper proposes the solution of these problems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The developed system has showed high reliability in diagnosing several seeded backlash levels in the robot.This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies.
Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. An experimental investigation was accomplished using the PUMA 560 robot.
#INTRODUCTION TO NEURAL NETWORKS USING MATLAB 6.0 SOFTWARE#
A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults (backlash) that could be progressed in the gearbox of industrial robot joints. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Industrial robots are commonplace in production systems and have long been used in order to improve productivity, quality and safety in automated manufacturing processes.
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