Results for 'Neural networks'

987 found
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  1. Artificial Neural Network for Forecasting Car Mileage per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 124:51-59.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with (...)
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  2.  55
    Using Neural Networks to Generate Inferential Roles for Natural Language.Peter Blouw & Chris Eliasmith - 2018 - Frontiers in Psychology 8:295741.
    Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which (...)
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  3.  11
    Neural Networks and Intellect: Using Model Based Concepts.Leonid I. Perlovsky - 2000 - Oxford, England and New York, NY, USA: Oxford University Press USA.
    This work describes a mathematical concept of modelling field theory and its applications to a variety of problems, while offering a view of the relationships among mathematics, computational concepts in neural networks, semiotics, and concepts of mind in psychology and philosophy.
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  4.  61
    A Neural Network Framework for Cognitive Bias.Johan E. Korteling, Anne-Marie Brouwer & Alexander Toet - 2018 - Frontiers in Psychology 9:358644.
    Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a (...) network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena. (shrink)
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  5.  76
    Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts.Truong-Son Nguyen, Le-Minh Nguyen, Satoshi Tojo, Ken Satoh & Akira Shimazu - 2018 - Artificial Intelligence and Law 26 (2):169-199.
    This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF (...)
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  6.  10
    (1 other version)Neural network methods for vowel classification in the vocalic systems with the [ATR] (Advanced Tongue Root) contrast.Н. В Макеева - 2023 - Philosophical Problems of IT and Cyberspace (PhilIT&C) 2:49-60.
    The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. (...)
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  7.  98
    Neural networks discover a near-identity relation to distinguish simple syntactic forms.Thomas R. Shultz & Alan C. Bale - 2006 - Minds and Machines 16 (2):107-139.
    Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and (...)
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  8. Some Neural Networks Compute, Others Don't.Gualtiero Piccinini - 2008 - Neural Networks 21 (2-3):311-321.
    I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
    the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
    Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
    compute in a non-classical way. (4) Yet other neural networks (...)
     
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  9.  63
    Artificial Neural Networks in Medicine and Biology.Helge Malmgren - unknown
    Artificial neural networks (ANNs) are new mathematical techniques which can be used for modelling real neural networks, but also for data categorisation and inference tasks in any empirical science. This means that they have a twofold interest for the philosopher. First, ANN theory could help us to understand the nature of mental phenomena such as perceiving, thinking, remembering, inferring, knowing, wanting and acting. Second, because ANNs are such powerful instruments for data classification and inference, their use (...)
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  10.  8
    Neural network methods for vowel classification in the vocalic systems with the [ATR] (Advanced Tongue Root) contrast.N. V. Makeeva - forthcoming - Philosophical Problems of IT and Cyberspace (PhilIT&C).
    The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. (...)
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  11. Artificial Neural Network for Predicting Car Performance Using JNN.Awni Ahmed Al-Mobayed, Youssef Mahmoud Al-Madhoun, Mohammed Nasser Al-Shuwaikh & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):139-145.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method (...)
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  12.  23
    Interacting neural networks and the emergence of social structure.Christina Stoica-Klüver & Jürgen Klüver - 2007 - Complexity 12 (3):41-52.
  13.  20
    The Resting-State Neural Network of Delay Discounting.Fan Yang, Xueting Li & Ping Hu - 2022 - Frontiers in Psychology 13:828929.
    Delay discounting is a common phenomenon in daily life, which refers to the subjective value of a future reward decreasing as a function of time. Previous studies have identified several cortical regions involved in delay discounting, but the neural network constructed by the cortical regions of delay discounting is less clear. In this study, we employed resting-state functional magnetic resonance imaging (RS-fMRI) to measure the spontaneous neural activity in a large sample of healthy young adults and used the (...)
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  14. Diabetes Prediction Using Artificial Neural Network.Nesreen Samer El_Jerjawi & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 121:54-64.
    Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial (...) networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)
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  15.  24
    Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems.Jing Bai & Yongguang Yu - 2018 - Complexity 2018:1-10.
    Due to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, a corresponding fractional-order adaption law is designed to approach the ideal neural network weight matrix of the unknown nonlinear terms, and a control law is (...)
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  16.  21
    Neural Network-Based Sensor Fault Accommodation in Flight Control System.T. V. Rama Murthy & Seema Singh - 2013 - Journal of Intelligent Systems 22 (3):317-333.
    This article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection. Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of (...)
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  17.  56
    Deep problems with neural network models of human vision.Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, Milton Llera Montero, Christian Tsvetkov, Valerio Biscione, Guillermo Puebla, Federico Adolfi, John E. Hummel, Rachel F. Heaton, Benjamin D. Evans, Jeffrey Mitchell & Ryan Blything - 2023 - Behavioral and Brain Sciences 46:e385.
    Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job (...)
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  18. Glass Classification Using Artificial Neural Network.Mohmmad Jamal El-Khatib, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (23):25-31.
    As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...)
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  19.  12
    Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional.Iván Sandoval-Palis, David Naranjo, Raquel Gilar-Corbi & Teresa Pozo-Rico - 2020 - Frontiers in Psychology 11.
    The purpose of this study is to train an artificial neural network model for predicting student failure in the academic leveling course of the Escuela Politécnica Nacional of Ecuador, based on academic and socioeconomic information. For this, 1308 higher education students participated, 69.0% of whom failed the academic leveling course; besides, 93.7% of the students self-identified as mestizo, 83.9% came from the province of Pichincha, and 92.4% belonged to general population. As a first approximation, a neural network model (...)
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  20.  36
    A Neural Network Approach to Obsessive- Compulsive Disorder.Dan J. Stein & Eric Hollander - 1994 - Journal of Mind and Behavior 15 (3):223-238.
    A central methodological innovation in cognitive science has been the development of connectionist or neural network models of psychological phenomena. These models may also comprise a theoretically integrative and methodologically rigorous approach to psychiatric phenomena. In this paper we employ connectionist theory to conceptualize obsessive-compulsive disorder . We discuss salient phenomenological and neurobiological findings of the illness, and then reformulate these using neural network models. Several features and mechanisms of OCD may be explicated in terms of disordered (...). Neural network modeling appears to constitute a novel and potentially fertile approach to psychiatric disorders such as OCD. (shrink)
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  21. Intelligent Neural Networks.J. Schank - 1982 - In Werner Leinfellner, Language and Ontology. Hölder-Pichler-Tempsky / Reidel. pp. 381--6.
     
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  22.  28
    Adaptive Neural Network Control of Serial Variable Stiffness Actuators.Zhao Guo, Yongping Pan, Tairen Sun, Yubing Zhang & Xiaohui Xiao - 2017 - Complexity:1-9.
    This paper focuses on modeling and control of a class of serial variable stiffness actuators based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and (...)
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  23.  10
    Convolutional neural networks reveal differences in action units of facial expressions between face image databases developed in different countries.Mikio Inagaki, Tatsuro Ito, Takashi Shinozaki & Ichiro Fujita - 2022 - Frontiers in Psychology 13.
    Cultural similarities and differences in facial expressions have been a controversial issue in the field of facial communications. A key step in addressing the debate regarding the cultural dependency of emotional expression is to characterize the visual features of specific facial expressions in individual cultures. Here we developed an image analysis framework for this purpose using convolutional neural networks that through training learned visual features critical for classification. We analyzed photographs of facial expressions derived from two databases, each (...)
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  24.  9
    Neural Networks and Neuroscience.Sidney J. Segalowitz & Daniel Bernstein - 1997 - In David Martel Johnson & Christina E. Erneling, The future of the cognitive revolution. New York: Oxford University Press. pp. 209.
  25. Neural Networks-Fast Kernel Classifier Construction Using Orthogonal Forward Selection to Minimise Leave-One-Out Misclassification Rate.X. Hong, S. Chen & C. J. Harris - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 4113--106.
  26.  56
    A neural network for creative serial order cognitive behavior.Steve Donaldson - 2008 - Minds and Machines 18 (1):53-91.
    If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler (...)
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  27. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.M. Kosinski & Y. Wang - 2018 - Journal of Personality and Social Psychology 114.
  28. Neural networks: they do not have to be complex to be complex.Irving Kupfermann - 1992 - Behavioral and Brain Sciences 15 (4):767-768.
     
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  29.  6
    Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations.Evan M. Russek, Frederick Callaway & Thomas L. Griffiths - 2024 - Cognitive Science 48 (11):e70015.
    Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data. Using these data, we train a neural network to invert the model, making it (...)
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  30.  18
    Neural Network-Based Output Feedback Fault Tolerant Tracking Control for Nonlinear Systems with Unknown Control Directions.Kun Yan, Chaobo Chen, Xiaofeng Xu & Qingxian Wu - 2022 - Complexity 2022:1-14.
    In this study, an adaptive output feedback fault tolerant control scheme is proposed for a class of multi-input and multioutput nonlinear systems with multiple constraints. The neural network is adopted to handle the unknown nonlinearity by means of its superior approximation capability. Based on it, the state observer is designed to estimate the unmeasured states, and the nonlinear disturbance observer is constructed to tackle the external disturbances. In addition, the Nussbaum function is utilized to cope with the actuator faults, (...)
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  31.  24
    Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness.Ying Zhao, Qinghua Zhu & Jiujun Bai - 2021 - Complexity 2021:1-11.
    This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used (...)
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  32.  97
    Ontology, neural networks, and the social sciences.David Strohmaier - 2020 - Synthese 199 (1-2):4775-4794.
    The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks have made their (...)
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  33. Large neural networks for the resolution of lexical ambiguity.Jean Véronis & Nancy Ide - 1995 - In Patrick Saint-Dizier & Evelyn Viegas, Computational lexical semantics. New York: Cambridge University Press. pp. 251--269.
  34. Neural Network Applications-Face Recognition Using Probabilistic Two-Dimensional Principal Component Analysis and Its Mixture Model.Haixian Wang & Zilan Hu - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 4221--337.
     
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  35. Neural Networks and Statistical Learning Methods (III)-The Application of Modified Hierarchy Genetic Algorithm Based on Adaptive Niches.Wei-Min Qi, Qiao-Ling Ji & Wei-You Cai - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 3930--842.
  36. Neural Network Models for Chaotic-Fuzzy Information Processing.Harold Szu, Joe Garcia, Lotfi Zadeh, Charles C. Hsu & Joseph DeWitte - 1994 - In Karl H. Pribram, Origins: Brain and Self Organization. Lawrence Erlbaum.
  37.  39
    Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller.Juntao Fei & Xiao Liang - 2018 - Complexity 2018:1-12.
    An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme (...)
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  38.  21
    Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots.Xuejing Lan, Zhenghao Wu, Wenbiao Xu & Guiyun Liu - 2018 - Complexity 2018:1-8.
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  39.  14
    Neural Networks: Test Tubes to Theorems.Leon N. Cooper, Mark F. Bear, Ford F. Ebner & Christopher Scofield - 1990 - In J. McGaugh, Jerry Weinberger & G. Lynch, Brain Organization and Memory: Cells, Systems, and Circuits. Guilford Press.
  40.  9
    Neural network ensembles: evaluation of aggregation algorithms.P. M. Granitto, P. F. Verdes & H. A. Ceccatto - 2005 - Artificial Intelligence 163 (2):139-162.
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  41.  17
    On Neural Network Solutions for the Ancient Game of GO.Ernest L. McDuffie, Marwan S. Al-Haik, Gunjan Gupta & Xingming Liu - 2001 - Journal of Intelligent Systems 11 (3):203-215.
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  42.  60
    Neural networks, AI, and the goals of modeling.Walter Veit & Heather Browning - 2023 - Behavioral and Brain Sciences 46:e411.
    Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.
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  43. Neural networks and psychopathology: an introduction.Dan J. Stein Andjacques Ludik - 1998 - In Dan J. Stein & Jacques Ludik, Neural Networks and Psychopathology: Connectionist Models in Practice and Research. Cambridge University Press.
     
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  44.  38
    Neural networks and psychopharmacology.Sbg Park - 1998 - In Dan J. Stein & Jacques Ludik, Neural Networks and Psychopathology: Connectionist Models in Practice and Research. Cambridge University Press. pp. 57.
  45.  39
    The brain, the artificial neural network and the snake: why we see what we see.Carloalberto Treccani - forthcoming - AI and Society:1-9.
    For millions of years, biological creatures have dealt with the world without being able to see it; however, the change in the atmospheric condition during the Cambrian period and the subsequent increase of light, triggered the sudden evolution of vision and the consequent evolutionary benefits. Nevertheless, how from simple organisms to more complex animals have been able to generate meaning from the light who fell in their eyes and successfully engage the visual world remains unknown. As shown by many psychophysical (...)
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  46.  17
    Neural networks ensembles approach for simulation of solar arrays degradation process.Vladimir Bukhtoyarov, Eugene Semenkin & Andrey Shabalov - 2012 - In Emilio Corchado, Vaclav Snasel, Ajith Abraham, Michał Woźniak, Manuel Grana & Sung-Bae Cho, Hybrid Artificial Intelligent Systems. Springer. pp. 186--195.
  47.  23
    Artificial Neural Networks and Fuzzy Neural Networks for Solving Civil Engineering Problems.Milos Knezevic, Meri Cvetkovska, Tomáš Hanák, Luis Braganca & Andrej Soltesz - 2018 - Complexity 2018:1-2.
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  48. A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots.Yiming Jiang, Chenguang Yang, Jing Na, Guang Li, Yanan Li & Junpei Zhong - 2017 - Complexity:1-14.
    As an imitation of the biological nervous systems, neural networks, which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot (...)
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  49.  36
    Neural Network for Complex Systems: Theory and Applications.Chenguang Yang, Jing Na, Guang Li, Yanan Li & Junpei Zhong - 2018 - Complexity 2018:1-2.
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  50. A neural network model for attentional enhancement of visual locations.D. Laberge, M. Carter & V. Brown - 1990 - Bulletin of the Psychonomic Society 28 (6):485-486.
     
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