Comparison of Control Strategies Applied to Nonlinear Quarterly Car Passive Suspension System

Gaya, Muhammad Sani; Bature, Amir A.; Yusuf, Lukman; Madugu, I. S.; Abubakar, Ukashatu; Abubakar, Saidu Adamu

doi:10.15866/ireaco.v8i3.5833

Cited by 2 publications

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“…The classification of the neural networks are based on their architecture as feed-forward or recurrent neural network [14,19]. Feed-forward neural network as shown in Figure 3 is the most commonly used for modelling and control because of its stable nature and simplicity [20,21]. This paper utilizes feed-forward neural network for the forecasting.…”

Section: Feed-forward Neural Network (Ffnn)mentioning

confidence: 99%

Medium term load demand forecast of Kano zone using neural network algorithms

2020

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Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand.

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