In this paper, an analogue model of a memristor using a light-dependent resistor (LDR) is presented. This model can be simplified into two parts: a control circuit and a variable resistor. It can be used to easily verify theoretical presumptions about the switching properties of memristors. This LDR-based memristor model can also be used in both simulations and experiments for future research into memristor applications. The paper includes mathematical models, simulations, and experimental results.

structure, different characteristics in loading and unloading processes of the submersible linear motor were model proposed had an excellent performance in failure diagnosis of the submersible pumping system.

This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.

Due to the nonlinear and nonstationary of river water turbidity, a novel hybrid forecasting model based on phase space reconstruction and support vector regression (PSR-SVR) is proposed. Firstly, the embedding dimension is chosen by using the false nearest neighbor method, and the time delay is obtained by the average mutual information. The phase space is reconstructed from the time series with the embedding dimension and the time delay got. The reconstructed time array is used as the input signal of support vector regression network. Then the forecasting model is established. Utilizing the model to forecast the river water turbidity, and it shows the accuracy of this new forecasting model is superior to RBF and BP forecasting methods.

No abstract