Intrusion Detection Using Hybrid Long Short-Term Memory with Binary Particle Swarm Optimization for Cloud Computing Systems

Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.

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An improved particle swarm optimization algorithm applied to long short-term memory neural network for ship motion attitude prediction

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219860731 ◽

2019 ◽

Vol 41 (15) ◽

pp. 4462-4471 ◽

Cited By ~ 1

Author(s):

Xiuyan Peng ◽

Biao Zhang ◽

Haiguang Zhou

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Particle Swarm Optimization Algorithm ◽

Short Term Memory ◽

Particle Swarm ◽

Ship Motion ◽

Short Term ◽

Swarm Optimization ◽

Term Memory ◽

Long Short Term Memory

This paper proposes a prediction method of ship motion attitude with high accuracy based on the long short-term memory neural network. The model parameters should be initialized randomly, resulting in critical decreases of the nonlinear learning ability of current parameter optimization methods. Therefore, a multilayer heterogeneous particle swarm optimization is proposed to optimize the parameters of long short-term memory neural network and applied to the prediction of ship motion. In multilayer heterogeneous particle swarm optimization, this paper proposes the concept of attractors, transforms the speed update equation, enhances the information interaction ability between particles, improves the optimization performance of the particle swarm optimization algorithm, and improves its optimization effect on the parameters of the long short-term memory networks. In the simulations, the measured data were used as input to predict the results of the ship motion. The results showed that the proposed method offers higher learning accuracy, faster convergence speed, and better prediction performance for accurate estimation of ship motion attitude than existing methods.

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