Sensor errors prediction using neural networks

Sachenko, Anatoliy; Kochan, Volodymyr; Turchenko, Volodymyr; Golovko, Vladimir; Savitsky, J.; Dunets, A.; Laopoulos, T.

doi:10.1109/ijcnn.2000.860811

Cited by 43 publications

(23 citation statements)

References 10 publications

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“…Inspired from biological nervous systems and brain structure, ANNs [16][17][18] have been, over the recent decades, central sources of inspiration for a large number of original techniques covering a vast field of applications [19][20][21]. From a general point of view, ANN could be seen as information processing systems which use learning and generalization capabilities and are very adaptive.…”

Section: Artificial Neural Network Applications In Financementioning

confidence: 99%

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A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

432

200

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Artificial Neural Network Applications In Financementioning

confidence: 99%

“…In the fifth section, conclusions are discussed. At the end, due to the complexity of the necessary abbreviations in this paper, especially in Tables 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20, the key for their interpretation is presented in ''Appendix A''.…”

Section: Introductionmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

432

200

View full text Add to dashboard Cite

show abstract

“…Inspired from biological nervous systems and brain structure, Artificial Neural Networks (ANN) [4][5][6][7][8][9][10] have been, over the recent decades, central sources of inspiration for a large number of original techniques covering a vast field of applications [11][12][13]. From a general point of view, ANN could be seen as information processing systems taking advantage from learning and generalization capabilities, making these techniques and the issued systems adaptive.…”

Section: Introductionmentioning

confidence: 99%

Hybrid credit ranking intelligent system using expert system and artificial neural networks

et al. 2009

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The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

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“…However, well known prediction methods, for example 5-degree polynomial, curvilinear alignment and cubic splines do not provide satisfactory results [2, 4]. Using artificial intelligence methods, in particularly, neural networks are This work is supported by the European INTAS grant YSF 03-55-2493 more effective in this case [5][6][7].Prediction using neural networks is used very widely and in the same time improvement of prediction accuracy traditionally is reached by improvement of neural network structure, using different neurons' activation functions, training algorithms, etc [8]. However mentioned approaches often do not provide satisfactory results and therefore it is necessary to use methods of special forming of neural network training set.…”

mentioning

confidence: 96%

Parallel Algorithm of Enhanced Historical Data Integration Using Neural Networks

Turchenko

Triki

Grandinetti

et al. 2005

2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

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The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.Keywords -sensor drift, integration historical data, neural networks, coarse-grain parallel algorithm, dynamic mapping, computational grids. LINTRODUCTIONThe authors have shown in [1][2], that the error of modern sensor data acquisition systems is much less than sensor's error in many cases. The accuracy improvement of physical quantity measurement is provided by (i) sensor calibration using special calibrator or (ii) sensor's periodic testing by reference sensor directly on exploitation place [3]. The frequency of calibration/testing procedure is called as inter-testing interval. However operations implementing calibration/testing procedures are rather laborious. Sensor drift prediction during inter-testing interval can reduce the laboriousness tremendously [1]. However, well known prediction methods, for example 5-degree polynomial, curvilinear alignment and cubic splines do not provide satisfactory results [2, 4]. Using artificial intelligence methods, in particularly, neural networks are This work is supported by the European INTAS grant YSF 03-55-2493 more effective in this case [5][6][7].Prediction using neural networks is used very widely and in the same time improvement of prediction accuracy traditionally is reached by improvement of neural network structure, using different neurons' activation functions, training algorithms, etc [8]. However mentioned approaches often do not provide satisfactory results and therefore it is necessary to use methods of special forming of neural network training set. Two such methods, additional approximating neural network and integration of historical data using set of Integrating Historical Data Neural Networks (IHDNNs), have been proposed and experimentally investigated in [2,7,[9][10]. These methods allow considerably decreasing number of sensor calibration/testing by artificial increasing of the training set of predicting neural network. Experimental result...

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Sensor errors prediction using neural networks

Cited by 43 publications

References 10 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Hybrid credit ranking intelligent system using expert system and artificial neural networks

Parallel Algorithm of Enhanced Historical Data Integration Using Neural Networks

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