Abstract The estimation of state health (SOH) a lithium-ion battery (LIB) is great significance to system safety and economic development. This paper proposes SOH method based on the SSA-Elman model for first time. To improve correlation rates between features capacity, combining median absolute deviation filtering Savitzky–Golay proposed process data. Based aging characteristics LIB, five with...

In order to implement the national need for optimal allocation of power resources, load forecasting, as an important research topic, has theoretical and practical significance. The purpose this study is construct a prediction model considering climate factors based on large amount historical data, prove that accuracy related both regularity. results forecasting are affected by many factors, so ...

A context-based attention task is employed in this paper. An Elman-type recurrent neural network is utilized to extract and keep the context information, and only the reinforcement signal that indicates whether the answer is correct or not is given. Through this learning, the function of an associative memory is observed in the Elman-type neural network. Adaptive formation of the basins are exa...

After the major reasons of water bloom were analyzed, using the rough set theory and principal component analysis respectively to identify the main factors affecting the forecast algal blooms. On this basis, to take advantage of the Libsvm water bloom prediction model and Elman water bloom prediction model for the shortterm prediction of algal blooms phenomenon respectively. Obtained through th...

The condition monitoring data of gears is asymmetric distributed, moreover, sampling is usually conducted discontinuously in practice. Thus makes it difficult to predict gear remaining useful life accurately considering the two reasons above. In this paper, a fusion method is proposed using Elman Neural Network to modify residual series of grey model since Elman Neural Network performs better o...

This paper investigates the application of wavelet transform as a preprocessor for neural networks (NN) in identifying internal turn-to-turn faults in transformer windings. The faulty and normal signals generated by numerical simulation of ElectroMagnetic Transient Program (EMTP) are preprocessed using discrete wavelet transform (DWT). The mean values of the wavelet coefficients are input into ...

Simple recurrent networks (SRNs) in symbolic time-series prediction (e.g., language processing models) are frequently trained with gradient descent--based learning algorithms, notably with variants of backpropagation (BP). A major drawback for the cognitive plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer. Yet agents in natur...