An Adaptive Decomposition Ensemble Learning Approach for Metro Passenger Flow Forecasting

Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an efficient and robust forecasting approach due to the inherent randomness and variations of metro passenger flows. In this study, we present a novel adaptive decomposition ensemble learning approach to accurately forecast the volume of metro passenger flows that combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), a multilayer perceptron (MLP) network and a long short-term memory (LSTM) network. Our proposed decomposition ensemble learning approach consists of three important stages. The first stage applies VMD to decompose the metro passenger flow data into periodic components, deterministic components and volatility components. Then, we employ the SAIMA model to forecast the periodic component, the LSTM network to learn and forecast the deterministic component and the MLP network to forecast the volatility component. In the last stage, these diverse forecasted components are reconstructed by another MLP network. The empirical results show that our proposed decomposition ensemble learning approach not only has the best forecasting performance compared with the relevant benchmark models but also appears to be the most promising and robust based on the historical passenger flow data in the Shenzhen subway system and several standard evaluation measures.

A Multi-Layer Perceptron With a Hierarchical Prior for Operating State Recognition of Ship Propulsion Systems

To define more clearly vibration-related problems of ship propulsion systems, a procedure incorporating operating state recognition into conventional vibration analysis is proposed in this paper. Emphasis is placed on identifying operating modes and decay levels through a multi-layer perceptron (MLP) with a hierarchical prior. First, a variant of stochastic gradient descent (SGD) with momentum is presented for integrating a hierarchical prior into the parameter learning of an MLP network. Then, the MLP network, governing information representation through multiple levels of abstraction is designed, and the hierarchical prior, representing a clear explanation in physics of system operating for an operator or maintainer, is also constructed. Finally, the operating data from a combined diesel or gas turbine (CODOG) system validate that the accuracy improvement of operating state recognition can be achieved by MLP with a hierarchical prior when the sample size is relatively small. Meanwhile, the vibration signals from the CODOG system verify the effectiveness of the vibration analysis procedure coupled with operating state recognition.

MLP Based Tan-Sigmoid Activation Function for Cardiac Activity Monitoring

Cardiac abnormalities can occur to everyone, irrespective of race, age or gender. However, family history gives a clear signal of the probable probability of heart failure in the heart. Cardiac abnormalities rarely show early symptoms, thereby contributing to sudden deaths in patients. In general, heartbeat is an irregular electric boost or heart activity. In this paper, an early monitoring system for detecting cardiac abnormalities was conducted using the Multilayer Perceptron (MLP) network. The cardiac abnormalities dataset is taken from the MIT-BIH database used to train the MLP network by using multiple training algorithms with Tan-Sigmoid as an activation function.

Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Experimental and artificial neural network investigation on the effect of inclination angle on the interface temperature of CPU/metal foam heat sink

PurposeThis study aims to clarify the relationship between inclination angle of hot surface of CPU and its temperature in absence and presence of aluminum foam as a cooling system. It proposes application of the artificial neural [multi-layer perceptron (MLP) and radial basis function] networks and adaptive neuron-fuzzy inference system (ANFIS) to predict interface temperature of central processing unit (CPU)/metal foam heat sink.Design/methodology/approachTo provide a consistent set of data, the surface of an aluminum cone with and without installing Duocel aluminum foam was heated in a natural convection using an electrical resistor. The hot surface temperature was measured using five K-type thermocouples (±0.1°C). To develop the predictive models, ambient temperature, input power and inclination angle are taken as input which varied from 23°C to 32°C, 4 to 20 W and 0° to 90°, respectively. The hot surface temperature is taken as the output.FindingsThe results show that in the presence of foam, the hot surface temperature was less sensitive to the variations of angle, and the maximum enhancement of the heat transfer coefficient was 23 per cent at the vertical position. Both MLP network and ANFIS are comparable, but the values predicted by MLP network are in more conformity with the measured values.Originality/valueThe effect of metal foam on the inclination angle/hot surface temperature dependence is identified. The optimum angle is clarified. The applicability of the MLP networks to predict interface temperature of CPU/heat sink is approved.