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Artificial neural network is an important research direction in data mining. It is used to solve classification and regression problems, and can find out the nonlinear relation between the input attribute and the output attribute, especially the smooth and continuous nonlinear relations. Use the Microsoft neural network to find out how the meteorological factors influence the precipitation, and to...
In order to optimizing the rock mass mechanical parameters of Xi-Luodu hydropower station, the neural network is improved by coupling the partial least-squares regression. And so the coupling method has best explaining ability to the system, Solving the problem that neural network model is not stable and the calculation velocity is slow, and overcoming the bad effect of the many layers relativity...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
Neural networks are often selected as tool for software effort prediction because of their capability to approximate any continuous function with arbitrary accuracy. A major drawback of neural networks is the complex mapping between inputs and output, which is not easily understood by a user. This paper describes a rule extraction technique that derives a set of comprehensible IF-THEN rules from a...
In this paper, two modeling approaches (artificial neural network and regression model) are established and used to predict the fiber diameter of melt blowing nonwovens. By analyzing the results of the models, the effects of process parameters on fiber diameter can be predicted. The results demonstrated that the ANN model yields more accurate and stable predictions than regression model, which is...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
We present a model using back-forward feed propagation method for tree diameter distribution in stand based on artificial neural network with three-tier management. This model is able to describe dynamics of diameter distribution in stands with high precision and good correlation. We also demonstrate that an ANN model from it has superiority in regression due to its ability to overcome some difficulties...
Regression-type algorithms are widely used for system modeling and characterization. There are applications where such characterizations are to be performed “on-line” to support control mechanisms and other decisions. In embedded autonomous systems robustness considerations ask for techniques, which, in addition to reflecting the actual state of the system and its environment, can continuously provide...
Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (Pb) and the oil formation volume factor (Bob) are important in the primary and subsequent development of an oil field. This paper proposes Support...
Recently Extreme Learning Machine (ELM) has been attracting attentions for its simple and fast training algorithm, which randomly selects input weights. Given sufficient hidden neurons, ELM has a comparable performance for a wide range of regression and classification problems. However, in this paper we argue that random input weight selection may lead to an ill-conditioned problem, for which solutions...
This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial...
Solar energy is one of the most promising renewable energy sources. The generating capacity of this source however is highly dependent on the available sunlight, its duration and intensity. In order to integrate these types of sources into an existing power distribution system, system planners need an accurate model that predicts its generating capacity with the usage of easily accessible information...
A general model of oil consumption is investigated in this study. This model should be applicable to different countries even with different characteristics. Conventional model that works very well for Malaysia is tested with seven developing Asian countries: China, India, Indonesia, Malaysia, Pakistan, Philippines, and Thailand. The results have shown that this model can not produce a good fit to...
In construction cost forecasting system, a great many uncertain factors effect the cost decision-making, so it is difficult to do effective forecasting by using traditional methods such as time series approach, regression analysis. In this paper, a nonlinear model based on RBF neural network is presented. There are some ameliorated measures in leaning algorithm of radial basis function (RBF) neural...
In this paper, data mining technology is adopted to find correlations from massive production data to predict burning through point (BTP) of sintering process. A hybrid BTP prediction model is presented which is based on artificial neural network and multi-linear regression error compensation algorithm. In this model, the final prediction result is calculated based on both the prediction value from...
The artificial neural network (ANN) method is used to study the macroscopic model of an actual water distribution system. For the first time, the Ant Colony Optimization (ACO) algorithm is implemented to optimize the node numbers of the hidden layers in the ANN model. The ANN model contains two hidden layers with a maximum of 64 nodes per layer. Each node number in the hidden layers is transformed...
The combination of pyrolysis mass spectrometry (PyMS) and artificial neural networks (ANNs) can be used to quantify levels of penicillins in strains of Penicillium chrysogenum and ampicillin in spiked samples of Escherichia coli. Four P. chrysogenum strains (NRRL 1951, Wis Q176, P1, and P2) were grown in submerged culture to produce penicillins, and fermentation samples were taken aseptically and...
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