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Fuzzy Entropy Based Neuro-Wavelet Identifier-Cum-Quantifier for Discrimination of Gases/Odors

IEEE SENSORS JOURNAL 2011年第7期11卷 1548-1555页

作者： Kumar, Ravi Das, R. R. Mishra, V. N. Dwivedi, R. Banaras Hindu Univ Inst Technol Dept Elect Engn Varanasi 221005 Uttar Pradesh India

In this paper, a new approach to design of an odor/gas identifier-cum-quantifier is presented. Dynamic response curves of an oxygen-plasma treated thick-film tin oxide sensor array exposed to four different gases were subjected to continuous wavelet transform (CWT). Appropriate wavelet coefficients were selected using multiscale principal component analysis (MSPCA). Fuzzy entropy and fuzzy subsethood values were calculated for the individual odor/gas and for the particular concentration band of each odor/gas, respectively. The quantitative information was encoded in the fuzzy subsethood values of the particular concentration bands in the output feature space, whereas the fuzzy entropy values were used to normalize the training data set consisting of MSPCA selected wavelet coefficients. A feedforward neural network was trained with a backpropagation algorithm with the training data containing the wavelet coefficients normalized with fuzzy entropies of individual odors/gases. The target data set was made up of the fuzzy subsethood values of the particular concentration band. The proposed network achieved identification and quantification of odors/gases with a 100% success rate. Also, fuzzy entropy based normalization helped to achieve 100% identification/quantification with a reduced number of sensors in the array.

关键词： backpropagation algorithm fuzzy entropy multi-scale principal component analysis oxygen plasma tin oxide sensors wavelet transform

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Training a network of mobile neurons

Training a network of mobile neurons

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International Joint Conference on Neural Networks (IJCNN)

作者： Apolloni, Bruno Bassis, Simone Valerio, Lorenzo Univ Milan Dept Comp Sci I-20122 Milan Italy Univ Milan Dept Math I-20122 Milan Italy

ISBN: (纸本)9781424496365

We introduce a new paradigm of neural networks where neurons autonomously search for the best reciprocal position in a topological space so as to exchange information more profitably. The idea that elementary processors move within a network to get a proper position is borne out by biological neurons in brain morphogenesis. The basic rule we state for this dynamics is that a neuron is attracted by the mates which are most informative and repelled by ones which are most similar to it. By embedding this rule into a Newtonian dynamics, we obtain a network which autonomously organizes its layout. Thanks to this further adaptation, the network proves to be robustly trainable through an extended version of the back-propagation algorithm even in the case of deep architectures. We test this network on two classic benchmarks and thereby get many insights on how the network behaves, and when and why it succeeds.

关键词： Acceleration Benchmark testing Biological neural networks Dynamics Layout Neurons Newtonian dynamics Training backpropagation backpropagation algorithm mobile neurons multilayer neural network multilayer perceptrons neural network training topological space

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Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

Multinodal Load Forecasting in Power Electric Systems using ...

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1st International Conference on High Performance Structures and Materials Engineering

作者： Altran, Alessandra Bonato Minussi, Carlos Roberto Martins Lopes, Mara Lucia Chavarette, Fabio Roberto Peruzzi, Nelson Jose UNESP Univ Estadual Paulista Fac Engn Dept Elect Engn Ave Jose Carlos Rossi 1370 BR-15385000 Ilha Solteira SP Brazil UNESP Fac Engn Dept Math BR-15385000 Avenida SP Brazil UNESP Dept Exact Sci BR-13484900 Avenida SP Brazil

ISBN: (纸本)9783037850756

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

关键词： Multinodal Forecast of Electric Load Artificial Neural Networks backpropagation algorithm Radial Basis Function

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Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

Multinodal Load Forecasting in Power Electric Systems using ...

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2011 First International Conference on High Performance Structures and Materials Engineering (ICHPSM 2011)

作者： Alessandra Bonato Altran Carlos Roberto Minussi Mara Lucia Martins Lopes Fbio Roberto Chavarette Nelson Jos Peruzzi Faculty of Engeneering UNESP-Univ Estadual PaulistaDepartament of Electrical Engeneering Faculty of Engeneering UNESP-Univ Estadual PaulistaDepartament of Mathematics UNESP-Univ Estadual Paulista Department of Exact Science

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron,making use of radial basis functions as activation function and the backpropagation algorithm,as an algorithm to train the *** methodology allows you to make the prediction at various points in power system,considering different types of consumers(residential,commercial,industrial) of the electric grid,is applied to the problem short-term electric load forecasting(24 hours ahead).We use a database(Centralised Dataset- CDS) provided by the Electricity Commission de New Zealand to this work.

关键词： Multinodal Forecast of Electric Load Artificial Neural Networks backpropagation algorithm Radial Basis Function

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Analysis of international debt problem using artificial neural networks and statistical methods

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NEURAL COMPUTING & APPLICATIONS 2010年第8期19卷 1207-1216页

作者： Yazici, Berna Memmedli, Memmedaga Aslanargun, Atilla Asma, Senay Anadolu Univ Dept Stat TR-26470 Eskisehir Turkey

It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries' international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture.

关键词： Artificial neural network backpropagation algorithm Conjugate gradient method Quasi-Newton method Logistic and probit regression Rescheduling and non-rescheduling of the international debts

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A neural network-based approach for the prediction of urban SO₂ concentrations in the Istanbul metropolitan area

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INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION 2010年第4期40卷 301-321页

作者： Akkoyunlu, Atilla Yetilmezsoy, Kaan Erturk, Ferruh Oztemel, Ercan Yildiz Tech Univ Dept Environm Engn Fac Civil Engn TR-34220 Istanbul Turkey Bogazici Univ Fac Engn TR-34342 Istanbul Turkey Marmara Univ Fac Engn Dept Ind Engn TR-34722 Istanbul Turkey

A three-layer Artificial Neural Network (ANN) model was developed to forecast air pollution levels. The subsequent SO2 concentration (24-hour averaged) being the Output parameter of this study was estimated by seven input parameters such as preceding SO2 concentrations (24-hour averaged), average daily temperature, sea-level pressure, relative humidity, cloudiness, average daily wind speed and daily dominant wind direction. After backpropagation training combined with Principal Component Analysis (PCA), the proposed model predicted subsequent SO2 values based oil measured data. ANN testing Outputs were proven to be satisfactory with correlation coefficients of about 0.770, 0.744 and 0.751 for the winter, summer and overall data, respectively.

关键词： ANN artificial neural network BP backpropagation algorithm modelling meteorological data SO2

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IDENTIFICATION OF NONLINEAR DYNAMICS OF NUCLEAR POWER REACTOR USING ADAPTIVE FEEDFORWARD NEURAL NETWORK

IDENTIFICATION OF NONLINEAR DYNAMICS OF NUCLEAR POWER REACTO...

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The 2nd Session of 2010 Workshop of Pakistan Academy of Sciences

作者： Arshad H.Malik Aftab A.Memon Muhammad R.Khan Directorate of Post Graduate Studies Mehran University of Engineering and TechnologyJamshoroSindhPakistan Department of Telecommunication Engineering Mehran University of Engineering & TechnologyJamshoroSindh Pakistan Electrical Engineering Pakistan Atomic Energy CommissionPakistan

In this paper,the authors have attempted the identification of real world non-linear time-varying Pressurized Heavy Water Reactor(PHWR) *** nonlinear dynamics is identified using adaptive feedforward neural network *** neural network(ANN) has been introduced to solve PHWR nuclear power plant operation complexity in *** Multi-input single-output(MISO) neural systems are designed for the prediction of control rod reactivity,moderator reactivity,moderator level and actual reactor power based on control rod and moderator level dynamics.A severe transient has been imposed in training and prediction phases with proposed *** proposed ANN is developed in *** essence,the proposed adaptive feedforward neural network mimics and identifies the actual nonlinear time-varying dynamics of PHWR system for an operating PHWR-type nuclear power plant in *** proposed ANN is found highly efficient and *** performance of the proposed ANN is investigated and the predicted results are in good agreement with the measured results.

关键词： Nonlinear Dynamics MISO Identification backpropagation algorithm moderator system control rod system PHWR nuclear power plant

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Artificial Neural Networks Applied in Environmental Quality Assessment

Artificial Neural Networks Applied in Environmental Quality ...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Liu Yijun,Tang Jiali,Jiang Hongfen,Zhu Guangping,Yu Zhimin School of Computer Engineering Jiangsu Teachers University of Technology Changzhou,China

The problem of environmental quality assessment is a pattern recognition problem,and a well-trained ANN can exploit the underlying nonlinear relationships that determine the environmental rating of a *** this study,we are trying with the neural network model to make an effective analysis for environmental quality assessment.A 4-9-1 threelayer feedforward neural network using the backpropagation and Levenberg-Marquardt algorithms has been used in the proposed artificial neural *** case study shows that this neural network is efficient and forms a useful tool for the prediction of environmental quality.

关键词： artificial neural network backpropagation algorithm Levenberg-Marquardt algorithm environmental quality assessment

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IDENTIFICATION OF NONLINEAR DYNAMICS OF NUCLEAR POWER REACTOR USING ADAPTIVE FEEDFORWARD NEURAL NETWORK

IDENTIFICATION OF NONLINEAR DYNAMICS OF NUCLEAR POWER REACTO...

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2010 Congress of Pakistan Academy of Sciences

关键词： Nonlinear Dynamics MISO Identification backpropagation algorithm moderator system control rod system PHWR nuclear power plant

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Telecommunication Fraud Prediction Using backpropagation Neural Network

Telecommunication Fraud Prediction Using Backpropagation Neu...

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International Conference of Soft Computing and Pattern Recognition

作者： Mohamed, Azlinah Bandi, Ahmad Fuad Mohamed Tamrin, Abdul Razif Jaafar, Md Daud Hasan, Suriah Jusof, Faeizah Univ Teknol MARA Fac Math & Comp Sci Shah Alam Selangor Malaysia TM Innovat Ctr Telekom Res & Dev Sdn Bhd Cyberjaya Malaysia

ISBN: (纸本)9781424453306

Telecommunication fraud prediction in this paper is an interpolation problem that uses the daily telecommunication network services information to analyze and predict the alarm information generated from a detection engine by minimizing false cases. This paper proposed the use of backpropagation neural network (BPNN) to perform telecommunication interpolation based on local telecommunication network services. The backpropagation algorithm is adjusted accordingly to control the speed of obtaining solution. The validation of this method is carried out following a series of experiments to establish the suitable BPNN parameters value for this interpolation problem. 20,000 cases were used and divided suitably into training and testing samples in analyzing the performance of the BPNN network. It is observed that the performance of BPNN in predicting fraud was merely 100% if the threshold value was set to 0.64 and above the predicted value. Consequently, the network model created could be used to analyze fraud risk classification and subsequently, provide contribution to the domain of fraud detection system.

关键词： Fraud detection system Telecommunication fraud backpropagation algorithm Call data records fraud risk clasification

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