Artificial Neural Network Modeling of MoS2 Supercapacitor for Predicative Synthesis
S. K. Kharade1, R. K. Kamat2, K. G. Kharade3
1S. K. Kharade*, Department of Mathematics, Shivaji University, Kolhapur, India.
2R. K. Kamat, Department of Computer Science, Shivaji University, Kolhapur, India.
3K. G. Kharade, Department of Computer Science, Shivaji University, Kolhapur, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 25 November, 2019. | Manuscript published on December 10, 2019. | PP: 554-560 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6516129219/2019©BEIESP | DOI: 10.35940/ijitee.B6516.129219
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Abstract: Energy storage systems are fundamental to the activity of intensity frameworks. They guarantee coherence of vitality supply and improve the dependability of the framework. The first area is centered on various energy storage frameworks, considering capacity limit, voltage and current proportions, and energy accessibility. Among the energy storage devices, supercapacitor is widely used because it is a high-limit capacitor with capacitance esteem a large amount than different capacitors. In the supercapacitor we have used MoS2 material synthesized with various Electrolytes. In perspective on the above mentioned, we report an Artificial Neural Network (ANN) strategy to achieve the predictable results. Levenberg- Marquardt feed-forward calculation prepares the neural network. We measure the exhibition of the ANN model with respect to mean square error (MSE) and the relationship coefficient between anticipated yield and yield given by the system. Results confirm the stability of supercapacitor over the other energy storage devices. To show such kind of conduct, we give Synthesis technique, Electrolyte, Cycle Life as an info esteems and Specific limit as yield esteem. For the amalgamation technique info esteem we have taken both compound and physical strategies by normalizing it. The practiced ANN demonstrating confirmations a higher number of concealed neuron design showing ideal execution as respects to expectation exactness
Keywords: ANN, Mean-Square Error, Simulation, Supercapacitor
Scope of the Article: Artificial Intelligent Methods, Models, Techniques