Comparison Study the Modeling of Limiting Current in the Magneto Electrodeposition of Vanadium using Neural-Wiener Model and Feed Forward Neural Network

Vanadium has long been used as a corrosion-resistant coating, including metal alloy for battery cathodes. However, batteries discovered with non-smooth cathode surfaces due to the fabrication process have short life. So, coating stage is required via electroplating method under influence of magnetic field or Magneto Electro Deposition (MED). Knowing limiting current in MED very important because optimum mass transport achieves at (iB). The smoothest and most compact electrodeposit surface will occur this current. In study, Feed Forward Neural Network Neural-Wiener are suggested compared nonlinear modeling approach determine ideal their strong capacity anticipate link between input output from experiment data. Levenberg-Marquadt optimization technique hidden neurons was evaluate compare capabilities two neural networks, Network, Wiener. results study presented comparison Mean Square Error (MSE) values obtained artificial network algorithms. algorithm that models lowest MSE value