“…The output is given by the following Eq. (3) [ 31 , 32 ]: where: y is the output of the current node of the hidden layer j, n is the number of entries of the current layer, x i is the entry of the current hidden layer of the previous layer i, w ji is the weight that connects the current and the previous layers, b j is the bias, and f is the activation function. Figure 1 Methodology for information processing using neurons.…”
“…The output is given by the following Eq. (3) [ 31 , 32 ]: where: y is the output of the current node of the hidden layer j, n is the number of entries of the current layer, x i is the entry of the current hidden layer of the previous layer i, w ji is the weight that connects the current and the previous layers, b j is the bias, and f is the activation function. Figure 1 Methodology for information processing using neurons.…”
“…L-M algorithm is an outstanding optimization method, which combines the characteristic of Gauss-Newton's method and the steepest descent algorithm [44]. On account of the considerable Energies 2018, 11, 995 6 of 18 performance in much research [45][46][47], it is chosen as the learning algorithm of the WNN.…”
State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test).
“…ANN modeling does not provide a tangible model that ensures the physical understanding of the underlying process in the material. The Multilayer Perceptron MLP is a feed forward network that guarantees multi-layer transmission via neurons (Tarbi et al 2017) as shown in Fig. 2.…”
Section: Artificial Neural Network Optimized By the Levenberg-marquarmentioning
The quaternaries In 1−x Ga x As y P 1−y are the main promising elements for the fabrication of optoelectronic devices. The adjustment of their physical parameters is assumed by the change of the molar fraction x and y. These parameters can be affected by the variation of temperature and pressure. To make the theoretical diagnosis of these materials, it is fundamental to know the energy gap ' E g ' and the lattice parameter ' a ', over a wide range of chemical compositions 0 ≤ x ≤ 0.47 and 0 ≤ y ≤ 1 , at different temperatures and pressures. We show that by using the Artificial Neural Network method optimized by the Levenberg Maquardt algorithm ANN-LM, it is possible to obtain results very close to the experiment. The scatter plot and error calculation show that the ANN-LM model provides more accurate values of the lattice parameter than those calculated by Vegard's law. On the other hand, the energy gap values Eg(x, y, T) estimated, using the ANN-LM model, proved to be close to the experimental values that those calculated by the empirical equations. In addition, the ANN-LM method allowed us to estimate with great accuracy the values of the energy gap at different temperatures and pressures Eg(P, T). Our work provides crucial information on the physical properties of the quaternary without the use of approximations, and without taking into account the hypothesis of a perfect agreement between InGaAsP and InP substrate.
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