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Aimed to the measuring problem of steam consumption in Dyeing process, a multiple neural network soft sensing modeling of Dyeing steam consumption based on adaptive fuzzy C-means clustering (FCM) is presented. The method is used for separating a whole real-time training data set into several clusters with different centers, and the clustering centers can been modified by an adaptive fuzzy clustering...
2D barcode region detection is a non trivial problem for barcode revognition and decoding especially in complex backgrounds. Currently, morphological processing has been widely applied to extract potential regions of Data Matrix barcodes due to its low computation complexity. However, this method leads to two problems, adaptive selection of morphological structuring element and high false accept rate...
As accurate identification of weeds from crops is the prerequisite for precise herbicides spraying, this paper proposes a multi-feature fusion method based on neutral network and D-S evidential theory to improve the accuracy of weed recognition. Firstly, three kinds of single features such as color, shape and texture are extracted from the weed and crop leaves after a series of image processing. Secondly,...
Combining the characteristics of drilling rig fault, a solution of fault diagnosis expert system based on artificial neural network is proposeed. The fault diagnosis system is designed for HT-60 drilling rig, which acquires knowledge by neural network and diagnoses by expert system. The system with characteristics of self-learning and self-adaptive can acquire knowledge from existing data in order...
Neural networks are increasingly used in the study of deformation control dam based on its self-organization, adaptive, self-learning, associative memory, a high degree of fault-tolerant, parallel processing abilities, a high degree of non-linear mapping capability, as well as linear dynamic characteristics. In this paper combines former research achievements, summarize and analyze the application...
In view of the complexity of Back Analysis of rock-fill material parameter, this paper uses genetic algorithm optimization BP neural network weights and threshold, simulated finite element calculation of rockfill dam by genetic neural network, combined with the theory of particle swarm optimization algorithm, and has realized inverse analysis of particle swarm optimization and genetic neural network...
The usual method of aerosol retrieval using remote sensing is interpolation of look-up-table (LUT), but it is too time-consuming. However, artificial neural network for nonlinear problem has been not applied widely for aerosol retrieval before. In this paper, aerosol optical depth (AOD) is retrieved using two methods: interpolation and neural network. Then, the retrieval capabilities of the two methods...
In this experiment, by using the method of artificial neural network and DPS DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana...
In this thesis, we will introduce the concepts of data mining technology and customer relationship management to analyze the advantages and disadvantages of decision tree and neural network. With the decision tree and neural network fusion algorithm, we shall find its necessity in bank-customers management system application in the banking sector development and will explain the detailed applications...
Because the traditional evaluation of the Automatic Test System (ATS) just considered the fuzzification of the evaluation index, and ignored the randomicity and indetermination of the index, so a new method based on neural net and cloud model is proposed to solve the problem. An ameliorated neural net algorithm is used to get the weight of the index. Cloud model is used to express the quantificational...
Because many complicate facts influence the slope stability of open pit mine, it is difficult to make sure the precise extent of the influence. Recently, people pay more attention to the Artificial Neural Network (ANN) for its powerful nonlinear fitting ability which can be applied to analyze stability of the slope. The consideration of effect on the stability of the open pit mine includes obliquity...
Coal mine water inrush is a very difficult problems which should be solved more earlier, and it have long plagued safe production of coal mines. The reliable operation of coal mine drainage systems plays a vital role in safe production of coal mines. Based on the non linear relationship of the state parameters of the coal mine drainage facilities, we propose a safety monitoring method based on BP...
JZQ250 gear box is studied in order to make real-time monitoring and fault diagnostics for the gearbox in engineering. With dynamic maximum speed limit set in particle swarm optimization (PSO), a method of diagnosing the gearbox's fault, i.e., the adaptive collaborative weighted velocity PSO (WVPSO) is suggested to train BP neural network. The fault diagnosis is made with the monitoring characteristic...
To address the drawbacks of slow convergenceand low accuracy of Double Parallel Feed-forward ProcessNeural Networks with output as function, an Elman-stylefeedback process neural network model with output as function is proposed. The learning algorithm of this model is given and the effectiveness of this model is proved by a non-linear system identification problem. .
Sizing yarn hairiness is an important yarn property like yarn evenness and strength. This property is affected by many factors such as fiber properties, sizing instruction, sizing device condition, sizing process and processing parameters etc., which makes its prediction difficult also. In this paper, in order to predict the sizing yarn hairiness of sizing process, an ANN model is developed. By analyzing...
To solve the traditional fault diagnosis can not be adapted to the complicated system, a kind of new multi-sensor fusion fault diagnosis method is presented. The method applies the theory of genetic algorithms and fuzzy logic to the BP (back propagation) neural network. Combined with BP and GA, it walks in several steps. Firstly, the best individual is chosen in current population and trained in order...
The advent of data mining has contributed significantly to the field of customer management. The payment center in Maersk finds that it's difficult to charge customers for the balance due. Thus the research on the application of artificial neural networks in classification of data mining will be conducted in this paper. Since the knowledge captured by neural networks is represented at a sub-symbolic...
Dynamic Difficulty Adjustment (DDA) can adjust game difficulty level dynamically; so it generates a tailor-made experience for each gamer. If a game is too easy, the gamer will feel bored; if it is too hard, the gamer will become frustrated. DDA is a mechanism to overcome this dilemma and augment the entertainment of a game by dynamically adjusting the parameters, scenarios and behaviors in the game...
Based on the qualitative analysis and research of implementing Reduce Vertical Separation Minimum (RVSM) to enhance the flight capacity in RVSM airspace all over the world, using systemic approaches that combine the qualitative analysis with ration analysis, this paper introduced mathematic description and neural network model of the air traffic issue in RVSM airspace. The mathematic description is...
Electroencephalogram (EEG) is the most important clinical tool in evaluating patients with epilepsy. However, the EEG definite patterns correlated to various types of epilepsy are still unclear. In this paper, six features of EEG signal are extracted to construct an artificial neural network model of classifying controls and patients with epilepsy. The ROC-score (area under curve) of the model is...
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