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Springback for multi-curvature part is a very important factor influencing the quality of sheet metal forming. Accurate calculation and controlling of springback are essential for the design of tools for sheet metal forming. In this paper, a springback quick compensation model is proposed to solve the problem of springback, which is based on fuzzy optimization improved GA-ANN algorithm and sheet metal...
Prediction of urban water demand is significant to urban water supply and treatment. To forecast urban water demand exactly, support vector machine optimized by genetic algorithm (GA-SVM) is proposed. Genetic algorithm(GA) is used to determine training parameters of support vector machine in GA-SVM. The experimental results indicate that the proposed GA-SVM model not only requires small training data,...
The paper presents a genetic neural network model based on the features of genetic algorithm and artificial neural network. It was applied to predict passenger capacity of China. The forecast result shows that genetic neural network model has a smaller margin of error than BP neural network model. Genetic neural network is rather effective than BP neural network. Using genetic neural network to predict...
Wind energy is rapidly emerging as a substantial contributor to the electricity generation capacity of utilities around the world. While the use of wind power both adds to the electricity supply and offers significant environmental benefits as a renewable source of energy, the stochastic nature of forces that produce wind energy prevents relying on it to meet base load requirements. Intermittent availability...
Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy...
This paper presents a systematic methodology for construction of high-level performance models using least squares support vector machine. The transistor sizes of the circuit-level implementation of a component block along with a set of geometry constraints applied over them define the sample space. Optimal values of the model hyper parameters are computed using genetic algorithm. The novelty of the...
In the study, back-propagation neural networks (BP-NN) theory and genetic algorithm (GA) were used to build a nonlinear prediction model reflecting the relationship between technics parameters of electric field aging and mechanical properties of LY12 aluminum alloy. In this model, electric field intensity, aging temperature and time were as input parameters. Tensile strength, yield strength and micro-yield...
The BP neural network algorithm has characteristics of slow convergence speed and local minimum value which could cause the loss of global optimal solution. In order to eliminate the shortcoming of BP neutral network algorithm, genetic algorithm is been put forward to optimize authority value and threshold value of BP nerve network. This paper establishes genetic neural network model. Study has been...
The use of neural networks (NNs) for financial applications is quite common because of their excellent performances of treating non-linear data with self-learning capability. Often arises the problem of a black-box approach,i.e. after having trained neural networks for a particular problem, it is almost impossible to analyse them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules...
In this paper we presented a genetic-based optimal input selection method. This method uses mutual information as similarity measure between variables and output. Based on mutual information the proper input variables, which describe the time series dynamics properly, will be selected. The selected inputs have a maximum relevance with output variable and there exists minimum redundancy between them...
This paper applied a genetic algorithm (GA) to optimize the parameters of support vector machine (SVM) for daily flow forecasting of Chickasaw creek located in Mobile County. To investigate the impact of variable enabling/disabling of flow, rainfall and evaporation on model prediction accuracy, four model structures with different input vectors were developed and the performance of them was evaluated...
Based on the data from the fatigue test of Epoxy Molding Compound (EMC), firstly with a focus on the application problem of the instability between fitting and prediction error of BP neural network (BPNN), the prediction model of fatigue life for EMC materials is established. In this approach, the network structure is improved with initiative way by reducing input from the perspective of nodes with...
In order to establish the forecasting model of the genetic-neural network, the genetic algorithm was used to optimize the connection weight and structure of the neural network through application of retaining the best individual in the genetic evolution process. This method overcomes the randomicity of the initial weight value, and, avoids the network oscillation as well as its being trapped into...
Load demand prediction is vital for maintaining stability and controlling risks of electricity market. An improved model which combines neural network with genetic algorithm is proposed to accurately predict load demand at equilibrium situation of day-ahead electricity market. In the proposed model, load demand prediction problem is converted into optimization problem of error minimization between...
A new improved Elman neural network, OAIF-Elman(output-add-input feedback Elman) network is presented based on the Elman neural network in this paper. The new Elman network is endowed with state-feedback and output-feedback by an additional output-context layer, which is added to the former input to get a new input of network. It is applied for setting up ethylene cracking severity soft sensor model,...
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