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This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
An improved genetic algorithm (Fuzzy Adaptive Simulated Annealing Genetic Algorithm with Gradient direction, GFASAGA) will be proposed in this paper, whose global superlinear convergence properties was analyzed by means by Markov chain etc. Certain fuzzy aeroengine compressor guide vane controller parameters of the regulator were optimized by GFASAGA, standard genetic algorithm (SGA) and customized...
A design method for bandwidth-efficient LDPC coded modulation for 22m-QAM constellations at rate (2m - 1)/(2m) in complex AWGN is presented. A multi-edge-type parameterization is used to exploit the distinct bit-channel capacities unique to high-order modulation using LDPC structures. EXIT analysis is adapted to multi-edge by introducing multi-dimensional EXIT iterated-function system analysis. Under...
The particle swarm optimization (PSO) algorithm is a swarm intelligence technique, which has exhibited good performance on finding optimal regions of complex search spaces. However, the basic PSO (bPSO) suffers from the premature convergence in multi-modal optimization. This is due to a decease of swarm diversity that leads to the global implosion and stagnation. It is an acceptable hypothesis that...
The particle swarm optimization (PSO) algorithmis a generally used optimal algorithm, which exhibits good performance on optimization problems in complex search spaces. However, traditional PSO model suffers from a local minima, and lacks of effective mechanism to escape from it. This is harmful to its overall performance. This paper presents an improved PSO model called the stochastic perturbing...
The ant colony algorithm is widely applied to optimize the complex problems in many fields with its features of being robust, parallel, flexible, demanding no artificial interference, and accurate. This paper discusses the application of the colony algorithm in the path search of the earthquake emergency rescue. We first construct a mathematical model for emergency rescue based on the earthquake disasters...
Improved clonal selection algorithms were proposed as a method to implement optimal iterative learning control algorithms. The strength of the method is that it not only can cope with non-minimum phase plants and nonlinear plants even there are uncertainties in their models, but also can deal with constraints on input signals conveniently by a specially designed mutation operator. Simulations show...
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