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A CMAC (Cerebellar Model Articulation Controller) network combined with PID (Proportion Integration Differential) control method was proposed in this paper. Aiming at the characteristics such as multivariable, strong coupling, nonlinear and time-varying parameters for the turbine regulating system, an improved mutative scale chaotic optimization algorithm was used for tuning the weight parameters...
Because calculation of stress intensity factors using the finite element method of the linear elastic fracture mechanics cannot satisfy the need for the real-time monitoring and the real-time analysis of cracks in concrete dams, a four-layer neural network for calculating stress intensity factors is proposed. The neural network is improved by chaos optimization algorithm. An example is given to validate...
In this paper a new optimization algorithm based on Chaos Optimization algorithm(COA) combined with traditional Baum Welch (BW) method is presented for training Hidden Markov Model (HMM) for Continues speech recognition. The BW algorithm easily trapped in local optimum, which might deteriorate the speech recognition rate, while an important character of COA is global search. so we can get a globally...
A novel neural network with chaotic property is proposed. The network is composed by different neurons. Some activation function is chaotic iterative function instead of the conventional Sigmoid function. In the process of learning, taking advantage of the randomicity property and ergodicity property of chaos, the generalization capability and the optimizing efficiency can be improved. The simulation...
Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos...
A novel application to the optimization of artificial neural networks (ANNs) is presented in this paper. Here, the weight and architecture optimization of ANNs can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the ANN. Finally, the optimized ANN is applied to the prediction...
The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. And the parameters selection is a procedure of global optimization search. Since the Differential Evolution (DE) population-based algorithm is a real coding optimal algorithm with powerful...
Groundwater level has random characters because of influences factors of natural and anthropogenic. Study random prediction model of groundwater level on the basis of groundwater physical process analysis is important to groundwater appraisal. The theory of supporting vector machine based on small-sample machine learning theory is introduced into dynamic prediction of groundwater level. A least square...
Forecasting weapon system cost accurately has great meaning in determining weapons' appropriate price, reducing the cost risk and raising the efficiency of equipment expense utilization. The least squares support vector machine (LSSVM) was applied to forecast weapon system cost, and the chaos optimization algorithm, which is regarded as a good optimization method, was used to optimize the penalty...
In this paper, artificial neural network is combined with wavelet analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data sequence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a chaos optimization neural network is established for...
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