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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Sahoo, M. K. Nayak, Janmenjoy Mohapatra, S. Nayak, B. K. Behera, H. S. Veer Surendra Sai Univ Technol Dept Comp Sci & Engn Sambalpur 768018 Odisha India

ISBN: (纸本)9788132222088;9788132222071

The use of artificial neural network technique has significantly improved the quality of pattern recognition day-by-day with better performance. After the evolution of optimization algorithms, it has given new directions for achieving efficient result. In this paper a firefly based back-propagation network has proposed for character recognition. The firefly algorithm is a nature inspired optimization algorithm and it is simulated into back-propagation algorithm to achieve faster and better convergence rate within few iteration. The characters are collected from system through mouse that are used for training and characters are collected from MS-Paint that are used for testing purpose. The performance is analyzed and it is observed that proposed method performs better and leads to converge in less number of iteration than back-propagation algorithm.

关键词： ANN back propagation Firefly back propagation algorithm

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Artificial neural network approach to assess selective flocculation on hematite and kaolinite

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International Journal of Minerals,Metallurgy and Materials 2014年第7期21卷 637-646页

作者： Lopamudra Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre R&D Tata Steel Limited Institute of Minerals and Materials Technology(Council of Scientific and Industrial Research) Department of Fuel & Minerals Engineering Indian School of Mines University

Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.

关键词： hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency

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Log-sum enhanced sparse deep neural network

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NEUROCOMPUTING 2020年 407卷 206-220页

作者： Qiao, Chen Shi, Yan Diao, Yu-Xian Calhoun, Vince D. Wang, Yu-Ping Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Georgia State Univ Triinst Ctr Translat Res Neuroimaging & Data Sci Georgia Inst Technol Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Tulane Univ Ctr Genom & Bioinformat New Orleans LA 70112 USA

How to design deep neural networks (DNNs) for the representation and analysis of high dimensional but small sample size data is still a big challenge. One solution is to construct a sparse network. At present, there exist many approaches to achieve sparsity for DNNs by regularization, but most of them are carried out only in the pre-training process due to the difficulty in the derivation of explicit formulae in the fine-tuning process. In this paper, a log-sum function is used as the regularization terms for both the responses of hidden neurons and the network connections in the loss function of the fine-tuning process. It provides a better approximation to the L-0-norm than several often used norms. Based on the gradient formula of the loss function, the fine-tuning process can be executed more efficiently. Specifically, the commonly used gradient calculation in many deep learning research platforms, such as PyTorch or TensorFlow, can be accelerated. Given the analytic formula for calculating gradients used in any layer of DNN, the error accumulated from successive numerical approximations in the differentiation process can be avoided. With the proposed log-sum enhanced sparse deep neural network (LSES-DNN), the spar-sity of the responses and the connections can be well controlled to improve the adaptivity of DNNs. The proposed model is applied to MRI data for both the diagnosis of schizophrenia and the study of brain developments. Numerical experiments demonstrate its superior performance among several classical classifiers tested. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Deep neural network Log-sum enhanced sparsity back propagation algorithm Concise gradient formula Magnetic resonance imaging.

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Prediction of calcium concentration in human blood serum using an artificial neural network

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MEASUREMENT 2011年第2期44卷 312-319页

作者： Neelamegam, P. Jamaludeen, A. Rajendran, A. SASTRA Univ Dept Elect & Instrumentat Engn Thanjavur 613402 Tamil Nadu India St Josephs Coll Autonomous PG Dept Elect Tiruchirappalli 620002 Tamil Nadu India Nehru Mem Coll Autonomous PG & Res Dept Phys Tiruchirappalli 621007 Tamil Nadu India

A predictive method, based on artificial neural network (ANN) has been developed to study absorbance and pH effects on the equilibrium of blood serum. This strategy has been used to analyze serum samples and to predict the calcium concentration in blood serum. A dedicated data acquisition system is designed and fabricated using a LPC2106 microcontroller with light emitting diode (LED) as source and photodiode as sensor to measure absorbance and to calculate the calcium concentration. A multilayer neural network with back propagation (BP) training algorithm is used to simulate different concentration of calcium (Ca2+) as a function of absorbance and pH, to correlate and predict calcium concentration. The computed calcium concentration by neural network is quite satisfactory with correlations R-2 = 0.998 and 0.995, standard errors of 0.0127 and 0.0122 in validation and testing stages respectively. Statistical analysis are carried out to check the accuracy and precision of the proposed ANN model and validation of results produce a relative error of about 3%. These results suggest that ANN can be efficiently applied and is in good agreement with values obtained with the current clinical spectrophotometric methods. Hence, ANN can be used as a complementary tool for studying metal ion complexion, with special attention to the blood serum analysis. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network back propagation algorithm Blood serum Calcium Microcontroller LED Photo diode

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Neural network applications for detecting process faults in packed towers

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2004年第7期43卷 841-847页

作者： Sharma, R Singh, K Singhal, D Ghosh, R Univ KwaZulu Natal Sch Chem Engn ZA-4041 Durban South Africa Malaviya Natl Inst Technol Dept Chem Engn Jaipur 302017 Rajasthan India

Artificial neural networks can be used as a fault diagnostic tool in chemical process industries. Connection strengths representing correlation between inputs (sensor measurements) and outputs (faults) are made to learn by the network using the back propagation algorithm. Results are presented for diagnostic faults in an ammonia-water packed distillation column. First, a 6-4-6 network architecture (six input nodes corresponding to the state variables and six output nodes corresponding to the six malfunctions) was chosen based on the minimum root-mean-square-error and mean absolute percentage error;and a maximum value of the Pearson correlation coefficient (C-P). The values of the learning rate, momentum and the gain terms were taken as 0.8, 0.8 and 1.0, respectively. The detection of the designated faults by the network was good. Relative importance of the various input variables on the output variables was calculated based on the partitioning of connection weights which showed that bottoms temperature, overhead composition and overhead temperature are not much affected by the disturbances in feed rate, feed composition and vapor rate in the given range. This resulted in a simplified 3-4-6 net architecture with similar capabilities as the 6-4-6 net thereby reducing the number of computations. (C) 2003 Elsevier B.V. All rights reserved.

关键词： artificial neural networks back propagation algorithm fault diagnosis packed towers mean absolute percentage error relative importance

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Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach

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COMPUTERS & STRUCTURES 2006年第26-27期84卷 1709-1718页

作者： Jeyasehar, C. Antony Sumangala, K. Annamalai Univ Dept Civil & Struct Engn Annamalainagar 608002 Tamil Nadu India

An artificial neural network (ANN) based approach is presented for the assessment of damage in prestressed concrete beams from natural frequency measurements. The details of an experimental programme suitably designed and carried out to induce the desired extents of damages in the prestressed concrete beams and generate the training and test data for the ANN are presented. The analysis of the static and dynamic behavior of perfect and damaged prestressed concrete beams reveal that there exists a close relationship among the natural frequency, deflection, crack width, first crack load, ultimate load and degree of damage. Therefore, these parameters were mainly used as input data for training and testing the ANN. A feed forward ANN learning by back propagation algorithm implemented using MATLAB has been employed in this study. The main focus of this work has been to study the feasibility of using an ANN trained with only natural frequency data to assess the damage in prestressed concrete beams. This is explored by comparing the performance of an ANN trained only with natural frequency data with other ANNs trained with a mix of static and dynamic data. It has been demonstrated that an ANN trained only with dynamic data can assess the damage with less than 10% error, when the error is the difference between the actual damage in percent and predicted damage in percent. The shortcomings of this study have also been presented. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： prestressed concrete damage assessment non-destructive testing natural frequency artificial neural network back propagation algorithm

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Classification of surface roughness and distance measurement using artificial neural network

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INSTRUMENTATION SCIENCE & TECHNOLOGY 2003年第4期31卷 417-423页

作者： Neelamegam, P Rajendran, A Nehru Mem Coll Dept Appl Phys Tiruchirappalli 621007 Tamil Nadu India AVVM Sri Pushpam Coll Dept Phys Thanjavur Tamil Nadu India

An automated AT89C55WD microcontroller based surface roughness and distance measurement system is developed. An IR diode and detector are used for the measurement. In this paper, the experimental arrangement for the measurement of distance and classification of surface roughness is explained. An artificial neural network (ANN), trained with a back propagation algorithm is employed for the data processing to classify the roughness of the surface and for the distance measurement. A three layer ANN is used to learn the type of surface and corresponding output using a back propagation algorithm. After training, the neural network is used to classify the roughness of the surface and to measure distance.

关键词： surface roughness artificial neural network back propagation algorithm microcontroller surface classification distance measurement

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Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks

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TALANTA 2013年 111卷 98-104页

作者： Monavar, H. Mohamadi Afseth, N. K. Lozano, J. Alimardani, R. Omid, M. Wold, J. P. Univ Tehran Fac Agr Engn & Technol Karaj *** Iran Norwegian Food Res Inst Nofima Mat N-1430 As Norway Univ Extremadura Escuela Ingn Ind Dept Ingn Elect Elect & Automat E-06071 Badajoz Spain

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely;Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with back propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R-2) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fa

关键词： Caviar Raman spectroscopy Principal component analysis Linear discriminant analysis Radial-basis function back propagation algorithm

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Perceptual audio coding using sinusoidal/optimum wavelet representation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2002年第5期21卷 511-524页

作者： Sathidevi, PS Venkataramani, Y Natl Inst Technol Dept Elect Engn Calicut 673601 Kerala India

A perceptual audio coder, in which each audio segment is adaptively analyzed using either a sinusoidal or an optimum wavelet basis according to the time-varying characteristics of the audio signals, has been constructed. The basis optimization is achieved by a novel switched filter bank scheme, which switches between a uniform filter bank structure (discrete cosine transform) and a non-uniform filter bank structure (discrete wavelet transform). A major artifact of the International ISO/Moving Pictures Experts Group (MPEG) audio coding standard (MPEG-1 layers 1 and 2) known as pre-echo distortion which uses a uniform filter bank structure for audio signal analysis, is almost eliminated in the proposed coder. A perceptual masking model implemented using a high-resolution wavelet packet filter bank with 27 subbands, closely mimicking the critical bands of the human auditory system, is employed in this audio coder. The resulting scheme is a variable bit-rate audio coder, which provides compression ratios comparable to MPEG-I layers 1 and 2 with almost transparent quality.

关键词： psychoacoustics discrete wavelet transform wavelet packets neural networks back propagation algorithm pre-echo distortion

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A method for COP prediction of an on-site screw chiller applied in cinema

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2019年 98卷 459-467页

作者： Tian, Chengcheng Xing, Ziwen Pan, Xi Tian, Yafen Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China China Northwest Architecture Design & Res Inst CO Xian 710018 Shaanxi Peoples R China

An artificial neural (ANN) model is developed under on-site operating conditions, and it is different within a laboratory. On-site testing carried out to gather amount of data is for the purpose of training and prediction. back propagation algorithm (BP) is applied to establish the transient coefficient of performance (COP) prediction model. The most suitable network structure is selected by comparison and cross-validation method, and the selected model is trained through setting the suitable network parameters. The error between the predicted results and the experimental data is almost within +/- 5.0% and the maximum error is 5.8%. Additionally, the validated model is used to evaluate the performance of a water-cooled variable frequency screw water chiller under various operating conditions and different openness of electronic expansion valve (EXV). It is valuable to apply neutral network on water-cooled screw chillers with maximum efficiency when satisfying a building cooling load instantly under necessary conditions. (C) 2018 Published by Elsevier Ltd.

关键词： Artificial neutral network back propagation algorithm On-site working, COP prediction Variable frequency screw chiller

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