Relevant bibliographies by topics / Competitive neural networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Competitive neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Competitive neural networks"

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Brunner, Daniel, and Demetri Psaltis. "Competitive photonic neural networks." Nature Photonics 15, no. 5 (2021): 323–24. http://dx.doi.org/10.1038/s41566-021-00803-0.

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Aiken, Milam. "Competitive Intelligence through neural networks." Competitive Intelligence Review 10, no. 1 (1999): 49–53. http://dx.doi.org/10.1002/(sici)1520-6386(199931)10:1<49::aid-cir7>3.0.co;2-z.

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Abas, Ahmed R. "Adaptive competitive learning neural networks." Egyptian Informatics Journal 14, no. 3 (2013): 183–94. http://dx.doi.org/10.1016/j.eij.2013.08.001.

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Gunhan, Atilla E., László P. Csernai, and Jørgen Randrup. "UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS." International Journal of Neural Systems 01, no. 02 (1989): 177–86. http://dx.doi.org/10.1142/s0129065789000086.

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We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.
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Fang, Luyuan, and Tao Li. "DESIGN OF COMPETITION-BASED NEURAL NETWORKS FOR COMBINATORIAL OPTIMIZATION." International Journal of Neural Systems 01, no. 03 (1990): 221–35. http://dx.doi.org/10.1142/s0129065790000126.

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A systematic approach to the design of neural networks for combinatorial optimization is presented in this paper. This approach adopts a methodology which is based on competition. The neural networks for optimization problem solving are connected using the competitive geometry. Our approach relies on the use of simple heuristics in network design. It is therefore easy to learn. The performance of such networks is also impressive. Two examples are also included in this paper to demonstrate our approach and to present results of performance study.
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KIM, JONGWAN, JESUNG AHN, and SEONGWON CHO. "ENSEMBLE COMPETITIVE LEARNING NEURAL NETWORKS WITH REDUCED INPUT DIMENSION." International Journal of Neural Systems 06, no. 02 (1995): 133–42. http://dx.doi.org/10.1142/s0129065795000111.

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Conventional neural networks utilize all the dimensions of the original input patterns for training and classification. However, a particular attribute of the input patterns does not necessarily contribute to classification and may even cause misclassification in certain cases. A new ensemble competitive learning method using the reduced input dimension is proposed. In contrast to the previous ensemble neural networks which adjust learning parameters, the proposed method takes advantage of the information in each dimension of the input patterns. Since the degree of contribution of each attribu
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Chaudhary, Vikas, Anil K. Ahlawat, and R. S. Bhatia. "Growing Neural Networks using Soft Competitive Learning." International Journal of Computer Applications 21, no. 3 (2011): 1–6. http://dx.doi.org/10.5120/2495-3372.

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Chen, Chuan, Ling Mi, Zhongqiang Liu, Baolin Qiu, Hui Zhao, and Lijuan Xu. "Predefined-time synchronization of competitive neural networks." Neural Networks 142 (October 2021): 492–99. http://dx.doi.org/10.1016/j.neunet.2021.06.026.

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Curto, Carina, Jesse Geneson, and Katherine Morrison. "Fixed Points of Competitive Threshold-Linear Networks." Neural Computation 31, no. 1 (2019): 94–155. http://dx.doi.org/10.1162/neco_a_01151.

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Threshold-linear networks (TLNs) are models of neural networks that consist of simple, perceptron-like neurons and exhibit nonlinear dynamics determined by the network's connectivity. The fixed points of a TLN, including both stable and unstable equilibria, play a critical role in shaping its emergent dynamics. In this work, we provide two novel characterizations for the set of fixed points of a competitive TLN: the first is in terms of a simple sign condition, while the second relies on the concept of domination. We apply these results to a special family of TLNs, called combinatorial thresho
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Meyer-Bäse, Anke, Frank Ohl, and Henning Scheich. "Singular Perturbation Analysis of Competitive Neural Networks with Different Time Scales." Neural Computation 8, no. 8 (1996): 1731–42. http://dx.doi.org/10.1162/neco.1996.8.8.1731.

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The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stabi
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Dissertations / Theses on the topic "Competitive neural networks"

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Wanous, Mohammed. "A neurofuzzy expert system for competitive tendering in civil engineering." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343683.

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Bloom, Filip. "Competitive Coevolution for micromanagement in StarCraft: Brood War." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15377.

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Context. Interest in and research on neural networks and their capacity for finding solutions to nonlinear problems has increased greatly in recent years. Objectives. This thesis attempts to compare competitive coevolution to traditional neuroevolution in the game StarCraft: Brood War. Methods. Implementing and evolving AI-controlled players for the game StarCraft and evaluating their performance. Results. Fitness values and win rates against the default StarCraft AI and between the networks were gathered. Conclusions. The neural networks failed to improve under the given circumstances. The be
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Balavoine, Aurèle. "Implementation of the locally competitive algorithm on a field programmable analog array." Thesis, Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37255.

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Sparse approximation is an important class of optimization problem in signal and image processing applications. This thesis presents an analog solution to this problem, based on the Locally Competitive Algorithm (LCA). A Hopfield-Network-like analog system, operating on sub-threshold currents is proposed as a solution. The results of the circuit components' implementation on the RASP2.8a chip, a Field Programmable Analog Array, are presented.
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Molz, Rolf Fredi. "Proposta de implementação em hardware dedicado de redes neurais competitivas com técnicas de circuitos integrados analógicos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1998. http://hdl.handle.net/10183/26305.

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Neste trabalho apresenta-se uma proposta de uma técnica para implementação em hardware, das estruturas básicas de uma Rede Neural Competitiva, baseada em técnicas analógicas. Através desta proposta, será abordada uma das classes mais interessantes de Redes Neurais Artificiais (RNA) que são as Redes Neurais Competitivas (RNC), que possuem forte inspiração biológica. As equações fundamentais que descrevem o comportamento da RNC foram derivadas de estudos interdisciplinares, a maioria envolvendo observações neurofisiológicas. O estudo do neurônio biológico, por exemplo, nos leva a clássica equaçã
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Chawdhary, Adit. "DevSOM: Developmental Learning in Self Organizing Feature Maps." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164888614564.

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Papacostantis, Evangelos. "Competitive co-evolution of trend reversal indicators using particle swarm optimisation." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/23929.

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Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisati
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Mattos, CÃsar Lincoln Cavalcante. "ComitÃs de Classificadores Baseados nas Redes SOM e Fuzzy ART com Sintonia de ParÃmetros e SeleÃÃo de Atributos via MetaheurÃsticas EvolucionÃrias." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7034.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior<br>O paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃs de modificaÃÃes impostas a algoritmos de aprendizado nÃo-supervisionado, tais como as redes Fuzzy ART e SOM, dando origem, respectivamente, Ãs arquiteturas ARTIE (ART in Ensembles) e MUSCLE
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cruz, Magnus Alencar da. "AvaliaÃÃo de redes neurais competitivas em tarefas de quantizaÃÃo vetorial:um estudo comparativo." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2016.

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nÃo hÃ<br>Esta dissertaÃÃo tem como principal meta realizar um estudo comparativo do desempenho de algoritmos de redes neurais competitivas nÃo-supervisionadas em problemas de quantizaÃÃo vetorial (QV) e aplicaÃÃes correlatas, tais como anÃlise de agrupamentos (clustering) e compressÃo de imagens. A motivaÃÃo para tanto parte da percepÃÃo de que hà uma relativa escassez de estudos comparativos sistemÃticos entre algoritmos neurais e nÃo-neurais de anÃlise de agrupamentos na literatura especializada. Um total de sete algoritmos sÃo avaliados, a saber: algoritmo K -mÃdias e as redes WTA, FSCL, S
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Souza, Luis Gustavo Mota. "Modelos lineares locais para identificaÃÃo de sistemas dinÃmicos usando redes neurais competitivas." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7267.

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nÃo hÃ<br>Nesta tese aborda-se o problema de identificaÃÃo de sistemas dinÃmicos sobre a Ãtica dos modelos locais, em que o espaÃo de entrada à particionado em regiÃes de operaÃÃo menores sobre as quais sÃo construÃdos modelos de menor complexidade (em geral, lineares). Este tipo de modelo à uma alternativa aos chamados modelos globais em que a dinÃmica do sistema à identificada usando-se uma Ãnica estrutura (em geral, nÃo-linear) que cobre todo o espaÃo de entrada. Assim, o tema alvo desta tese à o projeto de modelos lineares locais cujo espaÃo de entrada à particionado por meio do uso de al
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Filho, Luiz Soares de Andrade. "Projeto de classificadores de padrÃes baseados em protÃtipos usando evoluÃÃo diferencial." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14230.

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Nesta dissertaÃÃo à apresentada uma abordagem evolucionÃria para o projeto eciente de classificadores baseados em protÃtipos utilizando EvoluÃÃo Diferencial. Para esta finalidade foram reunidos conceitos presentes na famÃlia de redes neurais LVQ (Learning Vector Quantization, introduzida por Kohonen para classificaÃÃo supervisionada, juntamente com conceitos extraÃdos da tÃcnica de clusterizaÃÃo automÃtica proposta por Das et al. baseada na metaheurÃstica EvoluÃÃo Diferencial. A abordagem proposta visa determinar tanto o nÃmero Ãtimo de protÃtipos por classe, quanto as posiÃÃes correspondentes
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Books on the topic "Competitive neural networks"

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Focus Symposium on Adaptive, Cooperative and Competitive Processes in Systems Modeling, Design and Analysis (2000 Baden-Baden, Germany). Proceedings of the Focus Symposium on Adaptive, Cooperative and Competitive Processes in Systems Modeling, Design and Analysis. Edited by Murgu Alexandru 1962-, Lasker G. E. 1935-, International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (12th : 2000 : Baden-Baden, Germany). International Institute for Advanced Studies in Systems Research and Cybernetics, 2001.

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Li, Shuai, and Long Jin. Competition-Based Neural Networks with Robotic Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4947-7.

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Lemmon, Michael. Competitively inhibited neural networks for adaptive parameter estimation. Kluwer Academic, 1991.

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Lemmon, Michael. Competitively Inhibited Neural Networks for Adaptive Parameter Estimation. Springer US, 1991.

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Lemmon, Michael. Competitively Inhibited Neural Networks for Adaptive Parameter Estimation. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4044-1.

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Panzironi, Francesca. Networks. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.270.

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A network may refer to “a group of interdependent actors and the relationships among them,” or to a set of nodes linked by a web of interdependencies. The concept of networks has its origins in earlier philosophical and sociological ideas such as Jean-Jacques Rousseau’s “general will” and Émile Durkheim’s “social facts”, which adressed social and political communities and how decisions are mediated and ideas are structured within them. Networks encompass a wide range of theoretical interpretations and critical applications across different disciplines, including governance networks, policy net
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Jin, Long, and Shuai Li. Competition-Based Neural Networks with Robotic Applications. Springer, 2017.

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Book chapters on the topic "Competitive neural networks"

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Saberi, Iman, and Fathiyeh Faghih. "Self-Competitive Neural Networks." In Advances in Visual Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_2.

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Chiarantoni, Ernesto, Giuseppe Acciani, Girolamo Fornarelli, and Silvano Vergura. "Robust Unsupervised Competitive Neural Network by Local Competitive Signals." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_156.

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Gomez-Ruiz, J. A., J. Muñoz-Perez, E. Lopez-Rubio, and M. A. Garcia-Bernal. "Expansive and Competitive Neural Networks." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_41.

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Cho, Seongwon, Jaemin Kim, and Sun-Tae Chung. "Dynamic Competitive Learning." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_67.

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Lee, Tsu-Chang. "Competitive Signal Clustering Networks." In Structure Level Adaptation for Artificial Neural Networks. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4_4.

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Uchiyama, Toshio, and Hiroshi Tomiyasu. "Competitive Learning for Color Space Division." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_21.

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Bacciu, Davide, and Antonina Starita. "Competitive Repetition-suppression (CoRe) Learning." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_14.

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Watola, David A., and Jack L. Meador. "Competitive Learning in Asynchronous-Pulse-Density Integrated Circuits." In Analog VLSI Neural Networks. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3582-9_7.

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Kamimura, Ryotaro. "Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_13.

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Eggert, Julian, Joerg Deigmoeller, and Volker Willert. "Layered Motion Segmentation with a Competitive Recurrent Network." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15822-3_15.

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Conference papers on the topic "Competitive neural networks"

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Sutton, G. G. ,. III, J. A. Reggia, and J. M. Maisog. "Competitive learning using competitive activation rules." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137728.

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Kamimura, Ryotaro. "Controlled Competitive Learning: Extending Competitive Learning to Supervised Learning." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371225.

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Andalib, Arash. "Competitive Decomposition of Input Space in a Competitive Modular Multinet System." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371364.

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Kosko, B. "Stochastic competitive learning." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137718.

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Reggia. "Methods for deriving competitive activation mechanisms." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118609.

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Yin, H., R. Lengelle, and P. Gaillard. "Inverse-step competitive learning." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170505.

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Ding, Jin, Yong-zhi Sun, Ping Tan, and Yong Ning. "Detecting Communities in Networks Using Competitive Hopfield Neural Network." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489362.

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Galindo, P. L. "The Competitive Forward-Backward algorithm (CFB)." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950533.

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Sutton, G. G. ,. III, J. A. Reggia, and J. M. Maisog. "Supervised and reinforced competitive learning." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137626.

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Lemmon, M., and B. V. K. V. Kumar. "Competitive learning's global search property." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137968.

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Reports on the topic "Competitive neural networks"

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Perrone, Michael P. A Soft-Competitive Splitting Rule for Adaptive Tree-Structured Neural Networks. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264936.

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