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Yarn quality prediction plays an important role in modern textile production management. Due to the nonlinearity and non-stationarity of yarn quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and artificial neural networks (ANN), has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve...
Dynamic structure-based neural networks are being extensively applied in many fields of science and engineering. A novel dynamic structure-based neural network determination approach using orthogonal genetic algorithm with quantization is proposed in this paper. Both the parameter (the threshold of each neuron and the weight between neurons) and the transfer function (the transfer function of each...
In this paper Hinfin loop-shaping design method as the frequency domain approach was applied to aero-engine control system to overcome the problem of lack of design transparency in the traditional time domain approaches. The controller design method should be carried out in the frequency domain, via selection of the Hinfin loop-shaping weights, open or closed-loop singular value plots were shaped...
This paper proposed a topology clustering scheme based on genetic algorithm (GACluster) to improve the topology clustering performance in the course of constructing the hierarchical overlay multicast. The scheme firstly constructed the clustering feature space by normalizing the two routing metrics (overlay path delay and overlay node access bandwidth), and then implemented genetic algorithm for the...
This paper presents a multiple-objective routing optimization scheme based on genetic algorithm (GA) methodology to the metric tradeoff optimization problem between two routing metric cost (delay and bandwidth) in overlay network. Besides an effective tradeoff routing feature, the scheme applies genetic- crossover to the local-search operations in the optimized iterative course for better performance...
This paper proposes a data mining algorithm based on genetic algorithm and entropy for rule discovery called Genetic-Miner. The goal of Genetic-Miner is to discover classification rules in data sets. We have compared the performance of Genetic-Miner with other two well-known algorithms in six public domain data sets. The results showed that, Genetic-Miner is particularly advantageous when it is important...
This paper deals with clustering of segments of stock prices by using nonlinear modeling system for time series based on the genetic programming (GP). We apply the GP procedure in learning phase of the system where we improve the nonlinear functional forms to approximate the models used to generate time series. The variation of the individuals with relatively high capability in the pool can cope with...
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