Relevant bibliographies by topics / Bidirectional associative memory

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Bidirectional associative memory'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Bidirectional associative memory"

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Osana, Yuko, Motonobu Hattori, and Masafumi Hagiwara. "Chaotic Bidirectional Associative Memory." IEEJ Transactions on Electronics, Information and Systems 116, no. 7 (1996): 741–47. http://dx.doi.org/10.1541/ieejeiss1987.116.7_741.

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Chen, Songcan, Hang Gao, and Wei Yan. "Improved exponential bidirectional associative memory." Electronics Letters 33, no. 3 (1997): 223. http://dx.doi.org/10.1049/el:19970155.

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Tai, H. M., C. H. Wu, and T. L. Jong. "High-order bidirectional associative memory." Electronics Letters 25, no. 21 (1989): 1424. http://dx.doi.org/10.1049/el:19890950.

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Yingquan Wu and D. A. Pados. "A feedforward bidirectional associative memory." IEEE Transactions on Neural Networks 11, no. 4 (2000): 859–66. http://dx.doi.org/10.1109/72.857767.

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Bělohlávek, Radim. "Fuzzy logical bidirectional associative memory." Information Sciences 128, no. 1-2 (2000): 91–103. http://dx.doi.org/10.1016/s0020-0255(00)00044-x.

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Mishchenko, Kirill. "Capacity of bidirectional associative memory." Contemporary Engineering Sciences 8 (2015): 825–33. http://dx.doi.org/10.12988/ces.2015.55160.

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Jeng, Y. J., and C. C. Yeh. "Modified intraconnected bidirectional associative memory." Electronics Letters 27, no. 20 (1991): 1818. http://dx.doi.org/10.1049/el:19911129.

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Jeng, Y. J., C. C. Yeh, and T. D. Chiueh. "Generalised stable bidirectional associative memory." Electronics Letters 28, no. 15 (1992): 1396. http://dx.doi.org/10.1049/el:19920888.

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Baoyun, Wang, Zhang Qing, and He Zhenya. "New exponential bidirectional associative memory." Journal of Electronics (China) 13, no. 1 (1996): 56–60. http://dx.doi.org/10.1007/bf02684715.

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Zheng-Ou Wang. "A bidirectional associative memory based on optimal linear associative memory." IEEE Transactions on Computers 45, no. 10 (1996): 1171–79. http://dx.doi.org/10.1109/12.543710.

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Dissertations / Theses on the topic "Bidirectional associative memory"

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Johnson, Melissa. "A Spiking Bidirectional Associative Memory Neural Network." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42222.

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Spiking neural networks (SNNs) are a more biologically realistic model of the brain than traditional analog neural networks and therefore should be better for modelling certain functions of the human brain. This thesis uses the concept of deriving an SNN from an accepted non-spiking neural network via analysis and modifications of the transmission function. We investigate this process to determine if and how the modifications can be made to minimize loss of information during the transition from non-spiking to spiking while retaining positive features and functionality of the non-spiking network. By comparing combinations of spiking neuron models and networks against each other, we determined that replacing the transmission function with a neural model that is similar to it allows for the easiest method to create a spiking neural network that works comparatively well. This similarity between transmission function and neuron model allows for easier parameter selection which is a key component in getting a functioning SNN. The parameters all play different roles, but for the most part, parameters that speed up spiking, such as large resistance values or small rheobases generally help the accuracy of the network. But the network is still incomplete for a spiking neural network since this conversion is often only performed after learning has been completed in analog form. The neuron model and subsequent network developed here are the initial steps in creating a bidirectional SNN that handles hetero-associative and auto-associative recall and can be switched easily between spiking and non-spiking with minimal to no loss of data. By tying everything to the transmission function, the non-spiking learning rule, which in our case uses the transmission function, and the neural model of the SNN, we are able to create a functioning SNN. Without this similarity, we find that creating SNN are much more complicated and require much more work in parameter optimization to achieve a functioning SNN.
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Bragansa, John. "On the performance issues of the bidirectional associative memory." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17809.

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Orbán, Levente L. "Behavioural Studies and Computational Models Exploring Visual Properties that Lead to the First Floral Contact by Bumblebees." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30917.

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This dissertation explored the way in which bumblebees' visual system helps them discover their first flower. Previous studies found bees have unlearned preferences for parts of a flower, such as its colour and shape. The first study pitted two variables against each other: pattern type: sunburst or bull's eye, versus the location of the pattern: shapes appeared peripherally or centrally. We observed free-flying bees in a flight cage using Radio-Frequency Identification (RFID) tracking. The results show two distinct behavioural preferences: Pattern type predicts landing: bees prefer radial over concentric patterns, regardless of whether the radial pattern is on the perimeter or near the centre of the flower. Pattern location predicts exploration: bees were more likely to explore the inside of artificial flowers if the shapes were displayed near the centre of the flower, regardless of whether the pattern was radial or concentric. As part of the second component, we implemented a mathematical model aimed at explaining how bees come to prefer radial patterns, leafy backgrounds and symmetry. The model was based on unsupervised neural networks used to describe cognitive mechanisms. The results captured with the results of multiple behavioural experiments. The model suggests that bees choose computationally "cheaper" stimuli, those that contain less information. The third study tested the computational load hypothesis generated by the artificial neural networks. Visual properties of symmetry, and spatial frequency were tested. Studying free-flying bees in a flight cage using motion-sensitive video recordings, we found that bees preferred 4-axis symmetrical patterns in both low and high frequency displays.
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González, Marek. "Fuzzy neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234941.

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This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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Cholet, Stéphane. "Evaluation automatique des états émotionnels et dépressifs : vers un système de prévention des risques psychosociaux." Thesis, Antilles, 2019. http://www.theses.fr/2019ANTI0388/document.

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Les risques psychosociaux sont un enjeu de santé publique majeur, en particulier à cause des troubles qu'ils peuvent engendrer : stress, changements d'humeurs, burn-out, etc. Bien que le diagnostic de ces troubles doive être réalisé par un professionel, l'Affective Computing peut apporter une contribution en améliorant la compréhension des phénomènes. L'Affective Computing (ou Informatique Affective) est un domaine pluridisciplinaire, faisant intervenir des concepts d'Intelligence Artificielle, de psychologie et de psychiatrie, notamment. Dans ce travail de recherche, on s'intéresse à deux éléments pouvant faire l'objet de troubles : l'état émotionnel et l'état dépressif des individus.Le concept d'émotion couvre un très large champ de définitions et de modélisations, pour la plupart issues de travaux en psychiatrie ou en psychologie. C'est le cas, par exemple, du circumplex de Russell, qui définit une émotion comme étant la combinaison de deux dimensions affectives, nommées valence et arousal. La valence dénote le caractère triste ou joyeux d'un individu, alors que l'arousal qualifie son caractère passif ou actif. L'évaluation automatique des états émotionnels a suscité, dans la dernière décénie, un regain d'intérêt notable. Des méthodes issues de l'Intelligence Artificielle permettent d'atteindre des performances intéressantes, à partir de données capturées de manière non-invasive, comme des vidéos. Cependant, il demeure un aspect peu étudié : celui des intensités émotionnelles, et de la possibilité de les reconnaître. Dans cette thèse, nous avons exploré cet aspect au moyen de méthodes de visualisation et de classification pour montrer que l'usage de classes d'intensités émotionnelles, plutôt que de valeurs continues, bénéficie à la fois à la reconnaissance automatique et à l'interprétation des états.Le concept de dépression connaît un cadre plus strict, dans la mesure où c'est une maladie reconnue en tant que telle. Elle atteint les individus sans distinction d'âge, de genre ou de métier, mais varie en intensité ou en nature des symptômes. Pour cette raison, son étude tant au niveau de la détection que du suivi, présente un intérêt majeur pour la prévention des risques psychosociaux.Toutefois, son diagnostic est rendu difficile par le caractère parfois anodin des symptômes et par la démarche souvent délicate de consulter un spécialiste. L'échelle de Beck et le score associé permettent, au moyen d'un questionnaire, d'évaluer la sévérité de l'état dépressif d'un individu. Le système que nous avons développé est capable de reconnaître automatiquement le score dépressif d'un individu à partir de vidéos. Il comprend, d'une part, un descripteur visuel spatio-temporel bas niveau qui quantifie les micro et les macro-mouvements faciaux et, d'autre part, des méthodes neuronales issues des sciences cognitives. Sa rapidité autorise des applications de reconnaissance des états dépressifs en temps réel, et ses performances sont intéressantes au regard de l'état de l'art. La fusion des modalités visuelles et auditives a également fait l'objet d'une étude, qui montre que l'utilisation de ces deux canaux sensoriels bénéficie à la reconnaissance des états dépressifs.Au-delà des performances et de son originalité, l'un des points forts de ce travail de thèse est l'interprétabilité des méthodes. En effet, dans un contexte pluridisciplinaire tel que celui posé par l'Affective Computing, l'amélioration des connaissances et la compréhension des phénomènes étudiés sont des aspects majeurs que les méthodes informatiques sous forme de "boîte noire" ont souvent du mal à appréhender<br>Psychosocial risks are a major public health issue, because of the disorders they can trigger : stress, mood swings, burn-outs, etc. Although propoer diagnosis can only be made by a healthcare professionnel, Affective Computing can make a contribution by improving the understanding of the phenomena. Affective Computing is a multidisciplinary field involving concepts of Artificial Intelligence, psychology and psychiatry, among others. In this research, we are interested in two elements that can be subject to disorders: the emotional state and the depressive state of individuals.The concept of emotion covers a wide range of definitions and models, most of which are based on work in psychiatry or psychology. A famous example is Russell's circumplex, which defines an emotion as the combination of two emotional dimensions, called valence and arousal. Valence denotes an individual's sad or joyful character, while arousal denotes his passive or active character. The automatic evaluation of emotional states has generated a significant revival of interest in the last decade. Methods from Artificial Intelligence allow to achieve interesting performances, from data captured in a non-invasive manner, such as videos. However, there is one aspect that has not been studied much: that of emotional intensities and the possibility of recognizing them. In this thesis, we have explored this aspect using visualization and classification methods to show that the use of emotional intensity classes, rather than continuous values, benefits both automatic recognition and state interpretation.The concept of depression is more strict, as it is a recognized disease as such. It affects individuals regardless of age, gender or occupation, but varies in intensity or nature of symptoms. For this reason, its study, both at the level of detection and monitoring, is of major interest for the prevention of psychosocial risks.However, his diagnosis is made difficult by the sometimes innocuous nature of the symptoms and by the often delicate process of consulting a specialist. The Beck's scale and the associated score allow, by means of a questionnaire, to evaluate the severity of an individual's state of depression. The system we have developed is able to automatically recognize an individual's depressive score from videos. It includes, on the one hand, a low-level visual spatio-temporal descriptor that quantifies micro and macro facial movements and, on the other hand, neural methods from the cognitive sciences. Its speed allows applications for real-time recognition of depressive states, and its performance is interesting with regard to the state of the art. The fusion of visual and auditory modalities has also been studied, showing that the use of these two sensory channels benefits the recognition of depressive states.Beyond performance and originality, one of the strong points of this thesis is the interpretability of the methods. Indeed, in a multidisciplinary context such as that of Affective Computing, improving knowledge and understanding of the studied phenomena is a key point that usual computer methods implemeted as "black boxes" can't deal with
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Fan, Chih Lwan, and 范志倫. "Digital realization of exponential bidirectional associative memory." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/85425227658871310299.

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LIN, JIN-KUAN, and 林金寬. "The local training rule for bidirectional associative memory." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/88260896970828397222.

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Horng, In Hau, and 洪英豪. "Digital Bidirectional Associative Memory Using A Pseudo- Parallel Searching Approach." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/35949570990206343828.

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碩士<br>國立中山大學<br>電機工程研究所<br>83<br>In chapter 2 of this paper, we proposed a architecture which was one-bit fault tolerance bidirectional associative memory( BAM). We input the retrieval vector to the architecture. If any stored pattern is idential, then it output it''s associative stored vec- tor. If the first search can''t find any idential pattern, then the mask vector generator will invert one bit and keep on search- ing. In the end of the search, it will generate a succuessful or fail signal. In chapter 3 of this paper, we utilize pseudo- parallel searching method to ensure the fault tolerance of the BAM without much loss of the recall speed. The architecture for the implementation turns out to be scaleable, regular, and dense design.
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WU, JENG MING, and 吳振民. "Current-Model Implementation of Asymptotically Stable Exponential Bidirectional Associative Memory." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/11060740330390640096.

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碩士<br>國立中山大學<br>電機工程研究所<br>83<br>The most important purpose in this paper is to verify the implem- entation of eBAM. In chapter 1 of this paper we introduce the history of neural network. In chapter 2, by partition the evolut- ion function of eBAM, we can get lots of detail parts. Then use the current mode circuits to design the partial functions. In chpter 3, our designs are forcus on whole chip, so we discuss the interface of this chip. Chapter 4 is the summary about this paper . We proof the possibility of the implementaion of eBAM.
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LI, DONG-TAI, and 李東泰. "An application of bidirectional associative memory on object images recognition." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/03716453827106257085.

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Book chapters on the topic "Bidirectional associative memory"

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Humpert, B. "Bidirectional Associative Memory for Three Patterns." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_110.

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Shaposhnikov, Aleksey V., Andrey S. Ionisyan, and Anzor R. Orazaev. "Discrete Neural Network of Bidirectional Associative Memory." In Current Problems in Applied Mathematics and Computer Science and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34127-4_32.

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Johnson, Melissa, and Sylvain Chartier. "Increasing Accuracy in a Bidirectional Associative Memory through Expended Databases." In Artificial General Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09274-4_6.

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Hasegawa, K., and M. Hattori. "Improved Pseudo-Relaxation Learning Algorithm for Robust Bidirectional Associative Memory." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9_48.

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Ibarra-Picó, F., and J. M. García-Chamizo. "A generalized bidirectional associative memory with a hidden orthogonal layer." In ICANN ’94. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2097-1_101.

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Stamov, Gani, Stanislav Simeonov, Ivan Torlakov, and Marina Yaneva. "Parallel Technique on Bidirectional Associative Memory Cohen-Grossberg Neural Network." In Recent Contributions to Bioinformatics and Biomedical Sciences and Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31069-0_2.

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Sree Hari Rao, V., and P. Raja Sekhara Rao. "Bidirectional Associative Memory Neural Networks Involving Zones of No Activation/Dead Zones." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0023-3_22.

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Knoblauch, Andreas, and Günther Palm. "Bidirectional Associative Memory with Block Coding: A Comparison of Iterative Retrieval Methods." In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_1.

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Dewi, Fransisca Fortunata, Ford Lumban Gaol, and Tokuro Matsuo. "Bidirectional Associative Memory as Normalisator Backpropagation Neural Network in the Signature Image Training." In Innovations in Applied Informatics and Media Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30769-0_15.

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Garima and Priyanka Dahiya. "An Investigation into the Application of Bidirectional Associative Memory and Pseudorandom Number for Steganography." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8774-7_46.

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Conference papers on the topic "Bidirectional associative memory"

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Schurmann. "Generalized adaptive bidirectional associative memory." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118564.

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Wang, Cruz, and Mulligan. "An enhanced bidirectional associative memory." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118566.

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Humpert, B. "Bidirectional associative memory with several patterns." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137657.

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Yano, Yuichi, and Yuko Osana. "Chaotic Complex-Valued Bidirectional Associative Memory." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178640.

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Suzuki, Yozo, and Masaki Kobayashi. "Complex-valued bidirectional auto-associative memory." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706844.

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Kotur, Bhargava R. "Distance adaptation network's bidirectional associative memory." In 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT). IEEE, 2014. http://dx.doi.org/10.1109/icspct.2014.6884897.

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Leung, C. S., and K. F. Cheung. "Householder encoding for discrete bidirectional associative memory." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170410.

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Chartier, Sylvain, Gyslain Giguere, Patrice Renaud, Jean-Marc Lina, and Robert Proulx. "FEBAM: A Feature-Extracting Bidirectional Associative Memory." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371210.

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Srinivasan, V., and C. S. Chia. "Improving bidirectional associative memory performance by unlearning." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170760.

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Qadir, Omer, Jerry Liu, Jon Timmis, Gianluca Tempesti, and Andy Tyrrell. "Hardware architecture for a bidirectional hetero-associative Protein Processing Associative Memory." In 2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2011. http://dx.doi.org/10.1109/cec.2011.5949620.

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