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1

Kuzmanovski, Igor, and Marjana Novič. "Counter-propagation neural networks in Matlab." Chemometrics and Intelligent Laboratory Systems 90, no. 1 (2008): 84–91. http://dx.doi.org/10.1016/j.chemolab.2007.07.003.

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2

Drgan, Viktor, Katja Venko, Janja Sluga, and Marjana Novič. "Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models." International Journal of Molecular Sciences 25, no. 8 (2024): 4156. http://dx.doi.org/10.3390/ijms25084156.

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Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.
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3

Vracko, Marjan, Denise Mills, and Subhash C. Basak. "Structure-mutagenicity modelling using counter propagation neural networks." Environmental Toxicology and Pharmacology 16, no. 1-2 (2004): 25–36. http://dx.doi.org/10.1016/j.etap.2003.09.004.

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4

Zeinali, Yasha, and Brett Story. "Structural Impairment Detection Using Deep Counter Propagation Neural Networks." Procedia Engineering 145 (2016): 868–75. http://dx.doi.org/10.1016/j.proeng.2016.04.113.

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5

Hou, Xuan. "Research on Hyperspectral Data Classification Based on Quantum Counter Propagation Neural Network." Advanced Materials Research 546-547 (July 2012): 1377–81. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1377.

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It proposes the model and learning algorithm of Quantum Counter Propagation Neural Network and applies which in hyperspectral data classification as well. On one hand, introducing quantum theory into the structure or training process of Counter Propagation Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Counter Propagation Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. To complete the experiment of hyperspectral data classification with three ways and the result shows that effects of Quantum Counter Propagation Neural Network is superior to the traditional classification.
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6

Thai, Khac-Minh, and Gerhard F. Ecker. "Classification Models for hERG Inhibitors by Counter-Propagation Neural Networks." Chemical Biology & Drug Design 72, no. 4 (2008): 279–89. http://dx.doi.org/10.1111/j.1747-0285.2008.00705.x.

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7

Zupan, Jure, Marjana Novič, and Johann Gasteiger. "Neural networks with counter-propagation learning strategy used for modelling." Chemometrics and Intelligent Laboratory Systems 27, no. 2 (1995): 175–87. http://dx.doi.org/10.1016/0169-7439(95)80022-2.

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8

Chang, Chuan-Yu, Hung-Jen Wang, and Wen-Chih Shen. "Copyright-proving scheme for audio with counter-propagation neural networks." Digital Signal Processing 20, no. 4 (2010): 1087–101. http://dx.doi.org/10.1016/j.dsp.2009.12.001.

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9

Sygnowski, Wojciech. "Counter‐propagation neural network for image compression." Optical Engineering 35, no. 8 (1996): 2214. http://dx.doi.org/10.1117/1.600828.

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10

Wu, Cathy, and Sailaja Shivakumar. "Back-propagation and counter-propagation neural networks for phylogenetic classification of ribosomal RNA sequences." Nucleic Acids Research 22, no. 20 (1994): 4291–99. http://dx.doi.org/10.1093/nar/22.20.4291.

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11

Ballabio, Davide, Mahdi Vasighi, Viviana Consonni, and Mohsen Kompany-Zareh. "Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks." Chemometrics and Intelligent Laboratory Systems 105, no. 1 (2011): 56–64. http://dx.doi.org/10.1016/j.chemolab.2010.10.010.

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12

Drgan, Viktor, and Benjamin Bajželj. "Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs." International Journal of Molecular Sciences 22, no. 9 (2021): 4443. http://dx.doi.org/10.3390/ijms22094443.

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The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models.
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13

Mohammed, Saja. "Audio File Compression Using Counter Propagation Neural Network." AL-Rafidain Journal of Computer Sciences and Mathematics 7, no. 1 (2010): 153–68. http://dx.doi.org/10.33899/csmj.2010.163869.

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14

Rahmat, R. F., Y. T. A. Harahap, and D. Rachmawati. "Counter-propagation Neural Network for Brain Tumor Classification." Journal of Physics: Conference Series 1566 (June 2020): 012128. http://dx.doi.org/10.1088/1742-6596/1566/1/012128.

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15

Kawai, Shunsuke, and Satoshi Yamaguchi. "A Clastering Method for Incremental Learning using ESOINN and Counter Propagation Neural Networks." IEEJ Transactions on Electronics, Information and Systems 136, no. 7 (2016): 945–54. http://dx.doi.org/10.1541/ieejeiss.136.945.

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16

Zhang, Z., N. Zheng, and T. Wang. "Fuzzy generalization of the counter-propagation neural network: a family of soft competitive basis function neural networks." Soft Computing 5, no. 6 (2001): 440–50. http://dx.doi.org/10.1007/s005000100128.

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17

WU, CATHY H., HSI-LIEN CHEN, and SHENG-CHIH CHEN. "GENE CLASSIFICATION ARTIFICIAL NEURAL SYSTEM." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 501–10. http://dx.doi.org/10.1142/s0218213095000255.

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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (Protein Identification Resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (Ribosomal Database Project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed back-propagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.
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18

Mazurek, Sylwester, Thomas R. Ward, and Marjana Novič. "Counter propagation artificial neural networks modeling of an enantioselectivity of artificial metalloenzymes." Molecular Diversity 11, no. 3-4 (2007): 141–52. http://dx.doi.org/10.1007/s11030-008-9068-x.

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19

A., Sangita, and Vijay R. "Classification of Biomedical Images using Counter Propagation Neural Network." International Journal of Computer Applications 182, no. 10 (2018): 23–27. http://dx.doi.org/10.5120/ijca2018917714.

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20

Gautam, Chandan, and Vadlamani Ravi. "Counter propagation auto-associative neural network based data imputation." Information Sciences 325 (December 2015): 288–99. http://dx.doi.org/10.1016/j.ins.2015.07.016.

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21

Bajželj, Benjamin, and Viktor Drgan. "Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem." Molecules 25, no. 3 (2020): 481. http://dx.doi.org/10.3390/molecules25030481.

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Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology.
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22

Peterson, Keith L. "Quantitative Structure-Activity Relationships in Carboquinones and Benzodiazepines Using Counter-Propagation Neural Networks." Journal of Chemical Information and Modeling 35, no. 5 (1995): 896–904. http://dx.doi.org/10.1021/ci00027a017.

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23

Vracko, Marjan. "Kohonen Artificial Neural Network and Counter Propagation Neural Network in Molecular Structure-Toxicity Studies." Current Computer Aided-Drug Design 1, no. 1 (2005): 73–78. http://dx.doi.org/10.2174/1573409052952224.

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24

Belattar, Sara, Otman Abdoun, and El Khatir Haimoudi. "A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks." Journal of Communications Software and Systems 18, no. 1 (2022): 17–27. http://dx.doi.org/10.24138/jcomss-2021-0121.

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25

Rajasekaran, S. "Training-Free Counter Propagation Neural Network for Pattern Recognition of Fabric Defects." Textile Research Journal 67, no. 6 (1997): 401–5. http://dx.doi.org/10.1177/004051759706700603.

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We present an application of a Training-free counter propagation network (tfcpn) to detect fabric defects. The TFCPN, which is a modification of Hecht-Nielsen's counter propagation network (cpn), learns through a simple recording algorithm devoid of any training, while retaining the topology of the cpn model. The mathematical justification for the modification is also presented. Four kinds of fabric defects—neps, broken ends, broken picks, and oil stains—most likely to be found during weaving are considered for recognition by the network. Results show that fabric defects such as these inspected by means of image recognition in accordance with the tfcpn agree approximately with initial expectations. The cpn reported in this paper is training-free, and it can learn complicated textile design problems.
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26

Anandhi, K. "Image Recognition from Face Feature Descriptor Using Counter Propagation Neural Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (2017): 579–84. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0105.

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27

Kovács, László, and Gábor Terstyánszky. "Classification of Faults in Uncertain Regions Using Counter-Propagation Neural Network." IFAC Proceedings Volumes 33, no. 11 (2000): 393–97. http://dx.doi.org/10.1016/s1474-6670(17)37390-1.

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28

Chang, Chuan-Yu, Hung-Jen Wang, and Sheng-Jyun Su. "Copyright authentication for images with a full counter-propagation neural network." Expert Systems with Applications 37, no. 12 (2010): 7639–47. http://dx.doi.org/10.1016/j.eswa.2010.04.079.

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29

Hasegawa, Kiyoshi. "Nonlinear Modeling of Structure-Activity Data by Combining Genetic Algorithms and Counter Propagation Neural Networks." Journal of Computer Aided Chemistry 2 (2001): 11–20. http://dx.doi.org/10.2751/jcac.2.11.

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30

Thai, K. M., N. T. Huynh, T. D. Ngo, T. T. Mai, T. H. Nguyen, and T. D. Tran. "Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks." SAR and QSAR in Environmental Research 26, no. 2 (2015): 139–63. http://dx.doi.org/10.1080/1062936x.2014.995701.

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31

Peterson, Keith L. "Counter-propagation neural networks in the modeling and prediction of Kovats indexes for substituted phenols." Analytical Chemistry 64, no. 4 (1992): 379–86. http://dx.doi.org/10.1021/ac00028a011.

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32

Campbell, John L. E., and Keith E. Johnson. "Abductive networks: generalization, pattern recognition, and prediction of chemical behavior." Canadian Journal of Chemistry 71, no. 11 (1993): 1800–1804. http://dx.doi.org/10.1139/v93-223.

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Using commercially available software, it is possible to reduce numerical data to a mathematical representation called an abductive network (AN). In the current communication, we describe several simple examples which illustrate the interesting, and potentially useful properties of abductive networks. We show that when applied to the correlation of Kovats indices with molecular refractivities and dipole moments of substituted phenols, abductive networks more accurately predict Kovats indices than do counter-propagation neural networks or linear regression equations. When applied to the modeling of quantitative structure–activity relationships (QSAR) for local anesthetics, AN's are marginally superior to regression. AN's offer the advantage that correlations may be drawn between variables which are not easily related within a mathematical context.
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33

Jovanovic, Marija, Dragoslav Sokić, Iztok Grabnar, et al. "Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy." Journal of Pharmacy & Pharmaceutical Sciences 18, no. 5 (2015): 856. http://dx.doi.org/10.18433/j33031.

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Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were -6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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Stojković, Goran, Marjana Novič, and Igor Kuzmanovski. "Counter-propagation artificial neural networks as a tool for prediction of pKBH+ for series of amides." Chemometrics and Intelligent Laboratory Systems 102, no. 2 (2010): 123–29. http://dx.doi.org/10.1016/j.chemolab.2010.04.013.

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35

Stanojević, Mark, Marija Sollner Dolenc, and Marjan Vračko. "Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks." Toxics 11, no. 6 (2023): 486. http://dx.doi.org/10.3390/toxics11060486.

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Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the normal function of the human endocrine system. These chemicals can affect specific nuclear receptors, such as androgen receptors (ARs) or estrogen receptors (ER) α and β, which play a crucial role in regulating complex physiological processes in humans. It is now more crucial than ever to identify EDCs and reduce exposure to them. For screening and prioritizing chemicals for further experimentation, the use of artificial neural networks (ANN), which allow the modeling of complicated, nonlinear relationships, is most appropriate. We developed six models that predict the binding of a compound to ARs, ERα, or ERβ as agonists or antagonists, using counter-propagation artificial neural networks (CPANN). Models were trained on a dataset of structurally diverse compounds, and activity data were obtained from the CompTox Chemicals Dashboard. Leave-one-out (LOO) tests were performed to validate the models. The results showed that the models had excellent performance with prediction accuracy ranging from 94% to 100%. Therefore, the models can predict the binding affinity of an unknown compound to the selected nuclear receptor based solely on its chemical structure. As such, they represent important alternatives for the safety prioritization of chemicals.
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36

Drgan, Viktor, Špela Župerl, Katja Venko, Marjan Vračko, and Marjana Novič. "Counter-propagation artificial neural network models in read-across predictions of toxicity." Toxicology Letters 280 (October 2017): S283. http://dx.doi.org/10.1016/j.toxlet.2017.07.792.

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37

Singh, Uday Pratap, and Sanjeev Jain. "Modified Chaotic Bat Algorithm Based Counter Propagation Neural Network for Uncertain Nonlinear Discrete Time System." International Journal of Computational Intelligence and Applications 15, no. 03 (2016): 1650016. http://dx.doi.org/10.1142/s1469026816500164.

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Weight and bias connection are important features of neural networks, which is still challenging for researchers. In this work, we focus on initial weights and bias connection of counter propagation network (CPN) using modified chaotic bat algorithm (MCBA) i.e., MCBA-CPN for uncertain nonlinear systems and compare it with CPN using chaotic bat algorithm (CBA) i.e., CBA-CPN. Chaotic function is used for pulse frequency of bats in MCBA. We have implemented CBA and MCBA, which are based on the consideration of the global solution in the sound intensity adjustment. MCBA-CPN is applied on different uncertain nonlinear systems and Mackey–Glass time series data to test the concert in terms of prediction accuracy. Proposed method is validated through statistical testing like chi-square and [Formula: see text]-test demonstrate that the difference between target and output of proposed method are acceptable. Finally, MCBA-CPN is applied to a real world problem for prediction of milk production data.
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38

Xu, Shuwei, Shan Zhang, and Shuwei Xu. "Traffic marking recognition based on generating antagonistic neural network." E3S Web of Conferences 136 (2019): 04076. http://dx.doi.org/10.1051/e3sconf/201913604076.

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This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.
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39

Sakhre, Vandana, Sanjeev Jain, Vilas S. Sapkal, and Dev P. Agarwal. "Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/719620.

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Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.
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40

Stojić, Nataša, Slavica Erić, and Igor Kuzmanovski. "Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks." Journal of Molecular Graphics and Modelling 29, no. 3 (2010): 450–60. http://dx.doi.org/10.1016/j.jmgm.2010.09.001.

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41

Kuzmanovski, Igor, Marjana Novič, and Mira Trpkovska. "Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks." Analytica Chimica Acta 642, no. 1-2 (2009): 142–47. http://dx.doi.org/10.1016/j.aca.2009.01.041.

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42

Bhalke, D. G., C. B. Rama Rao, and D. S. Bormane. "Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network." Journal of Intelligent Information Systems 46, no. 3 (2015): 425–46. http://dx.doi.org/10.1007/s10844-015-0360-9.

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43

Ola, B. O., J. P. Oguntoye, O. O. Awodoye, and M. O. Oyewole. "Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network." International Journal of Computer Applications 175, no. 20 (2020): 19–26. http://dx.doi.org/10.5120/ijca2020920729.

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44

Hasegawa, Kiyoshi, Takehiro Hosoda, and Kimito Funatsu. "Data Mining of Structure-Activity Data Through Genetic Algorithm and Counter Propagation Neural Network." Journal of Computer Aided Chemistry 3 (2002): 90–98. http://dx.doi.org/10.2751/jcac.3.90.

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45

Chen, Bo-Hao, Shih-Chia Huang, and Jui-Yu Yen. "Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios." Neurocomputing 273 (January 2018): 481–93. http://dx.doi.org/10.1016/j.neucom.2017.08.002.

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46

Valkova, I., M. Vračko, and S. C. Basak. "Modeling of structure–mutagenicity relationships: counter propagation neural network approach using calculated structural descriptors." Analytica Chimica Acta 509, no. 2 (2004): 179–86. http://dx.doi.org/10.1016/j.aca.2003.12.035.

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47

Tsuji, Toshio, Masataka Nishida, Toshiaki Takahashi, and Koji Ito. "Gravity Compensation for Manipulator Control by Neural Networks with Partially Preorganized Structure." Journal of Robotics and Mechatronics 2, no. 4 (1990): 282–87. http://dx.doi.org/10.20965/jrm.1990.p0282.

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The gravity torque of a manipulator can be compensated if the equation of motion can be correctly introduced, but in general industrial manipulators, there are many cases when the parameter values such as the position of center of mass are not clear, and these values largely change by the exchange of hand portions and the grasping of substances. Furthermore, in addition to unclear parameters, there are factors which occur by structural gravity compensation (spring and counter-balance) and which in many cases are difficult to express with the equation of motion. In this paper, compensation of the gravity torque of the manipulator is studied by, the use of neural networks. For this purpose, a model which makes the structure known to be contained in mapping as a unit with preorganized characteristics prepared in parallel with hidden unit of error back propagation-type neural network is proposed, by which the characteristics of the link system which is the object for learning can be imbedded into the network as preorganized knowledge beforehand. Finally, the results of experiments done with the use of industrial manipulators are given, and it is made clear that the compensation of gravity torque of manipulator and adaptive learning for end-point load are possible by the use of this model.
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48

Drgan, V., Š. Župerl, M. Vračko, F. Como, and M. Novič. "Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm." SAR and QSAR in Environmental Research 27, no. 7 (2016): 501–19. http://dx.doi.org/10.1080/1062936x.2016.1196388.

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49

Neiband, M. S., A. Mani-Varnosfaderani, and A. Benvidi. "Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors." SAR and QSAR in Environmental Research 28, no. 2 (2017): 91–109. http://dx.doi.org/10.1080/1062936x.2017.1280535.

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Ravisankar, P., and V. Ravi. "Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP." Knowledge-Based Systems 23, no. 8 (2010): 823–31. http://dx.doi.org/10.1016/j.knosys.2010.05.007.

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