In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network...

This paper presents a novel approach for designing a linear phase digital high pass FIR filter using Improved Particle Swarm Optimization (IPSO) algorithm. Design of FIR f ilter is a multi-modal optimization problem. The conservative gradient based optimization techniques are not efficient for digital filter design. Given the specifications for the filters to be realized, IPSO algorithm generat...

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and the...

in this paper, a location allocation (la) problem in construction and demolition (c&d;) waste management (wm) is studied. a bi-level model for this problem under a fuzzy random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control c&d; waste and the after tre...

To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper proposes both the improved particle swarm optimization methods (IPSO) based on self-adaptive regulation strategy and the Chaos Theory. Given the effective balance of particles’ searching and development ability, the self-adaptive regulation strategy is employed to optimize the inertia weight. To im...

Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improve...

Extended Kalman filter (EKF) is widely used for speed estimation in sensorless vector control of induction motor. The major and unsolved issue in the practical implementation of the EKF is the choice of the process and measurement noise covariance matrices. In this paper, a speed estimation method using EKF optimized by improved particle swarm optimization (IPSO) is proposed. By combining the a...

In this paper an improved particle swarm optimization algorithm (IPSO) for electromagnetic applications is proposed. In order to overcome the drawbacks of standard PSO, some improved mechanisms for velocity updating, the exceeding boundary control, global best perturbation and the simplified quadratic interpolation (SQI) operator are adopted. To show the effectiveness of the proposed algorithm,...

To solve the problem that the parameters in grey neural network (GNN) are difficult to determine, the improved Particle Swarm Optimization (IPSO) algorithm is employed to search the optimums by the introduction of a threshold of velocity. When the particle velocity is less than the threshold, an accelerated momentum is applied on the particle to reinitialize the particle velocity and position. ...