FPGA Simulation Engine for Customized Construction of Neural Microcircuits
Blair, Hugh T.; Cong, Jason; Wu, Di
2014-01-01
In this paper we describe an FPGA-based platform for high-performance and low-power simulation of neural microcircuits composed from integrate-and-fire (IAF) neurons. Based on high-level synthesis, our platform uses design templates to map hierarchies of neuron model to logic fabrics. This approach bypasses high design complexity and enables easy optimization and design space exploration. We demonstrate the benefits of our platform by simulating a variety of neural microcircuits that perform oscillatory path integration, which evidence suggests may be a critical building block of the navigation system inside a rodent’s brain. Experiments show that our FPGA simulation engine for oscillatory neural microcircuits can achieve up to 39× speedup compared to software benchmarks on commodity CPU, and 232× energy reduction compared to embedded ARM core. PMID:25584120
A model of individualized canonical microcircuits supporting cognitive operations.
Directory of Open Access Journals (Sweden)
Tim Kunze
Full Text Available Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of basic elements, called canonical microcircuits. In this theoretical study we propose a novel canonical microcircuit model and find that it supports two basic computational operations: a gating mechanism and working memory. By means of bifurcation analysis we systematically investigate the dynamical behavior of the canonical microcircuit with respect to parameters that govern the local network balance, that is, the relationship between excitation and inhibition, and key intrinsic feedback architectures of canonical microcircuits. We relate the local behavior of the canonical microcircuit to cognitive processing and demonstrate how a network of interacting canonical microcircuits enables the establishment of spatiotemporal sequences in the context of syntax parsing during sentence comprehension. This study provides a framework for using individualized canonical microcircuits for the construction of biologically realistic networks supporting cognitive operations.
Stochastic computations in cortical microcircuit models.
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Stefan Habenschuss
Full Text Available Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
Stochastic computations in cortical microcircuit models.
Habenschuss, Stefan; Jonke, Zeno; Maass, Wolfgang
2013-01-01
Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
Neural Dynamics and Information Representation in Microcircuits of Motor Cortex
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Yasuhiro eTsubo
2013-05-01
Full Text Available The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs, in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.
Neural mechanisms of auditory categorization: from across brain areas to within local microcircuits
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Joji eTsunada
2014-06-01
Full Text Available Categorization enables listeners to efficiently encode and respond to auditory stimuli. Behavioral evidence for auditory categorization has been well documented across a broad range of human and non-human animal species. Moreover, neural correlates of auditory categorization have been documented in a variety of different brain regions in the ventral auditory pathway, which is thought to underlie auditory-object processing and auditory perception. Here, we review and discuss how neural representations of auditory categories are transformed across different scales of neural organization in the ventral auditory pathway: from across different brain areas to within local microcircuits. We propose different neural transformations across different scales of neural organization in auditory categorization. Along the ascending auditory system in the ventral pathway, there is a progression in the encoding of categories from simple acoustic categories to categories for abstract information. On the other hand, in local microcircuits, different classes of neurons differentially compute categorical information.
Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue
D’Angelo, Egidio; Antonietti, Alberto; Casali, Stefano; Casellato, Claudia; Garrido, Jesus A.; Luque, Niceto Rafael; Mapelli, Lisa; Masoli, Stefano; Pedrocchi, Alessandra; Prestori, Francesca; Rizza, Martina Francesca; Ros, Eduardo
2016-01-01
The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems. PMID:27458345
Layer-dependent attentional processing by top-down signals in a visual cortical microcircuit model
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Nobuhiko eWagatsuma
2011-07-01
Full Text Available A vast amount of information about the external world continuously flows into the brain, whereas its capacity to process such information is limited. Attention enables the brain to allocate its resources of information processing to selected sensory inputs for reducing its computational load, and effects of attention have been extensively studied in visual information processing. However, how the microcircuit of the visual cortex processes attentional information from higher areas remains largely unknown. Here, we explore the complex interactions between visual inputs and an attentional signal in a computational model of the visual cortical microcircuit. Our model not only successfully accounts for previous experimental observations of attentional effects on visual neuronal responses, but also predicts contrasting differences in the attentional effects of top-down signals between cortical layers: attention to a preferred stimulus of a column enhances neuronal responses of layers 2/3 and 5, the output stations of cortical microcircuits, whereas attention suppresses neuronal responses of layer 4, the input station of cortical microcircuits. We demonstrate that the specific modulation pattern of layer-4 activity, which emerges from inter-laminar synaptic connections, is crucial for a rapid shift of attention to a currently unattended stimulus. Our results suggest that top-down signals act differently on different layers of the cortical microcircuit.
Numerical studies of slow rhythms emergence in neural microcircuits: Bifurcations and stability
Komarov, M. A.; Osipov, G. V.; Suykens, J. A. K.; Rabinovich, M. I.
2009-03-01
There is a growing body of evidence that slow brain rhythms are generated by simple inhibitory neural networks. Sequential switching of tonic spiking activity is a widespread phenomenon underlying such rhythms. A realistic generative model explaining such reproducible switching is a dynamical system that employs a closed stable heteroclinic channel (SHC) in its phase space. Despite strong evidence on the existence of SHC, the conditions on its emergence in a spiking network are unclear. In this paper, we analyze a minimal, reciprocally connected circuit of three spiking units and explore all possible dynamical regimes and transitions between them. We show that the SHC arises due to a Neimark-Sacker bifurcation of an unstable cycle.
Directory of Open Access Journals (Sweden)
Athanasia Papoutsi
2014-07-01
Full Text Available Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON and termination (OFF and search for the minimum network size required for expressing these states within physiological regimes. We show that (a NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity that may facilitate the short-memory function of the PFC.
Osanai, Hisayuki; Minusa, Shunsuke; Tateno, Takashi
2018-02-10
Magnetic stimulation is widely used in neuroscience research and clinical treatment. Despite recent progress in understanding the neural modulation mechanism of conventional magnetic stimulation methods, the physiological mechanism at the cortical microcircuit level is not well understood due to the poor stimulation focality and large electric artifact in the recording. To overcome these issues, we used a sub-millimeter-sized coil (micro-coil) to stimulate the mouse auditory cortex in vivo. To determine the mechanism, we conducted the first direct electrophysiological recording of micro-coil-driven neural responses at multiple sites on the horizontal surface and laminar areas of the auditory cortex. The laminar responses of local field potentials (LFPs) to the magnetic stimulation reached layer 6, and the spatiotemporal profiles were very similar to those of the acoustic stimulation, suggesting the activation of the same cortical microcircuit. The horizontal LFP responses to the magnetic stimulation were evoked within a millimeter-wide area around the stimulation coil. The activated cortical area was dependent on the coil orientation, providing useful information on the effective position of the coil relative to the brain surface for modulating cortical circuitry activity. In addition, numerical calculation of the induced electric field in the brain revealed that the inhomogeneity of the horizontal electric field to the surface is critical for micro-coil-induced cortical activation. The results suggest that our micro-coil technique has the potential to be used as a chronic, less-invasive and highly focal neuro-stimulator, and is useful for investigating microcircuit responses to magnetic stimulation for clinical treatment. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.
Structured Connectivity Shapes Microcircuit Function in the Prefrontal Cortex.
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Stefanos Stefanou - Stamatiadis
2014-03-01
Full Text Available The application of new experimental techniques in vivo has shed light on the wiring diagram of cortical networks, revealing the highly non-random connectivity of pyramidal neurons. This structured connectivity is characterized by distance-dependent formation of neuronal clusters and over-represented structural ‘motifs’ (Perin 2011, Ko 2013. In the prefrontal cortex (PFC in particular, pyramidal neurons were shown to form hyper-clusters, compared to other sensory regions. Yet, very little is known about the functional properties of these microcircuits and their role in Persistent Activity (PA, a well known function of the PFC. PA is the spiking activity that persists beyond the stimulus presentation and is considered to be the cellular correlate of working memory. Although, PA was traditionally assumed to emerge in large scale networks, recent in vivo data in the PFC suggest that small microcircuits mediate its functional output (Durstwitz, 2010. Motivated by the above findings this work probes the role of realistic connectivity constraints in shaping the functional output of PFC, through simulations of biophysically and morphologically detailed PFC circuits. Towards this goal, we used a compartmental modeling approach, whereby layer 5 PFC pyramidal neurons are modeled with detailed morphological and biophysical properties. Three different types of interneurons were also implemented; the Fast-spiking (FS, Regular-spiking (RS, and Irregular-spiking (IS. These were biophysically detailed, yet morphologically simplified. Microcircuits consisted of 75 pyramidal neurons, 13 FS, 6 RS and 6 IS. Properties (location /number /amplitude /kinetics of both excitatory and inhibitory synapses were extensively validated against experimental data. The network model was used to investigate the effect of connectivity on the emergence of persistent activity. Two different connectivity profiles of pyramidal cells were implemented: one highly non
Energy Technology Data Exchange (ETDEWEB)
Skinner, F. K. [Toronto Western Research Institute, University Health Network, Krembil Discovery Tower, Toronto Western Hospital, 60 Leonard Street, 7th floor, 7KD411, Toronto, Ontario M5T 2S8 (Canada); Department of Medicine (Neurology), University of Toronto, 200 Elizabeth Street, Toronto, Ontario M5G 2C4 (Canada); Department of Physiology, University of Toronto Medical Sciences Building, 3rd Floor, 1 King' s College Circle, Toronto, Ontario M5S 1A8 (Canada); Ferguson, K. A. [Toronto Western Research Institute, University Health Network, Krembil Discovery Tower, Toronto Western Hospital, 60 Leonard Street, 7th floor, 7KD411, Toronto, Ontario M5T 2S8 (Canada); Department of Physiology, University of Toronto Medical Sciences Building, 3rd Floor, 1 King' s College Circle, Toronto, Ontario M5S 1A8 (Canada)
2013-12-15
There is an undisputed need and requirement for theoretical and computational studies in Neuroscience today. Furthermore, it is clear that oscillatory dynamical output from brain networks is representative of various behavioural states, and it is becoming clear that one could consider these outputs as measures of normal and pathological brain states. Although mathematical modeling of oscillatory dynamics in the context of neurological disease exists, it is a highly challenging endeavour because of the many levels of organization in the nervous system. This challenge is coupled with the increasing knowledge of cellular specificity and network dysfunction that is associated with disease. Recently, whole hippocampus in vitro preparations from control animals have been shown to spontaneously express oscillatory activities. In addition, when using preparations derived from animal models of disease, these activities show particular alterations. These preparations present an opportunity to address challenges involved with using models to gain insight because of easier access to simultaneous cellular and network measurements, and pharmacological modulations. We propose that by developing and using models with direct links to experiment at multiple levels, which at least include cellular and microcircuit, a cycling can be set up and used to help us determine critical mechanisms underlying neurological disease. We illustrate our proposal using our previously developed inhibitory network models in the context of these whole hippocampus preparations and show the importance of having direct links at multiple levels.
Skinner, F. K.; Ferguson, K. A.
2013-12-01
There is an undisputed need and requirement for theoretical and computational studies in Neuroscience today. Furthermore, it is clear that oscillatory dynamical output from brain networks is representative of various behavioural states, and it is becoming clear that one could consider these outputs as measures of normal and pathological brain states. Although mathematical modeling of oscillatory dynamics in the context of neurological disease exists, it is a highly challenging endeavour because of the many levels of organization in the nervous system. This challenge is coupled with the increasing knowledge of cellular specificity and network dysfunction that is associated with disease. Recently, whole hippocampus in vitro preparations from control animals have been shown to spontaneously express oscillatory activities. In addition, when using preparations derived from animal models of disease, these activities show particular alterations. These preparations present an opportunity to address challenges involved with using models to gain insight because of easier access to simultaneous cellular and network measurements, and pharmacological modulations. We propose that by developing and using models with direct links to experiment at multiple levels, which at least include cellular and microcircuit, a cycling can be set up and used to help us determine critical mechanisms underlying neurological disease. We illustrate our proposal using our previously developed inhibitory network models in the context of these whole hippocampus preparations and show the importance of having direct links at multiple levels.
Bullock, Daniel; Tan, Can Ozan; John, Yohan J.
2009-01-01
Abundant new information about signaling pathways in forebrain microcircuits presents many challenges, and opportunities for discovery, to computational neuroscientists who strive to bridge from microcircuits to flexible cognition and action. Accurate treatment of microcircuit pathways is especially critical for creating models that correctly predict the outcomes of candidate neurological therapies. Recent models are trying to specify how cortical circuits that enable planning and voluntary a...
Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding.
Directory of Open Access Journals (Sweden)
Aditya Gilra
Full Text Available Stimulus encoding by primary sensory brain areas provides a data-rich context for understanding their circuit mechanisms. The vertebrate olfactory bulb is an input area having unusual two-layer dendro-dendritic connections whose roles in odor coding are unclear. To clarify these roles, we built a detailed compartmental model of the rat olfactory bulb that synthesizes a much wider range of experimental observations on bulbar physiology and response dynamics than has hitherto been modeled. We predict that superficial-layer inhibitory interneurons (periglomerular cells linearize the input-output transformation of the principal neurons (mitral cells, unlike previous models of contrast enhancement. The linearization is required to replicate observed linear summation of mitral odor responses. Further, in our model, action-potentials back-propagate along lateral dendrites of mitral cells and activate deep-layer inhibitory interneurons (granule cells. Using this, we propose sparse, long-range inhibition between mitral cells, mediated by granule cells, to explain how the respiratory phases of odor responses of sister mitral cells can be sometimes decorrelated as observed, despite receiving similar receptor input. We also rule out some alternative mechanisms. In our mechanism, we predict that a few distant mitral cells receiving input from different receptors, inhibit sister mitral cells differentially, by activating disjoint subsets of granule cells. This differential inhibition is strong enough to decorrelate their firing rate phases, and not merely modulate their spike timing. Thus our well-constrained model suggests novel computational roles for the two most numerous classes of interneurons in the bulb.
Murray, John D; Anticevic, Alan; Gancsos, Mark; Ichinose, Megan; Corlett, Philip R; Krystal, John H; Wang, Xiao-Jing
2014-04-01
Excitation-inhibition balance (E/I balance) is a fundamental property of cortical microcircuitry. Disruption of E/I balance in prefrontal cortex is hypothesized to underlie cognitive deficits observed in neuropsychiatric illnesses such as schizophrenia. To elucidate the link between these phenomena, we incorporated synaptic disinhibition, via N-methyl-D-aspartate receptor perturbation on interneurons, into a network model of spatial working memory (WM). At the neural level, disinhibition broadens the tuning of WM-related, stimulus-selective persistent activity patterns. The model predicts that this change at the neural level leads to 2 primary behavioral deficits: 1) increased behavioral variability that degrades the precision of stored information and 2) decreased ability to filter out distractors during WM maintenance. We specifically tested the main model prediction, broadened WM representation under disinhibition, using behavioral data from human subjects performing a spatial WM task combined with ketamine infusion, a pharmacological model of schizophrenia hypothesized to induce disinhibition. Ketamine increased errors in a pattern predicted by the model. Finally, as proof-of-principle, we demonstrate that WM deteriorations in the model can be ameliorated by compensations that restore E/I balance. Our findings identify specific ways by which cortical disinhibition affects WM, suggesting new experimental designs for probing the brain mechanisms of WM deficits in schizophrenia.
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Hybrid microcircuit intraconnection processes
Energy Technology Data Exchange (ETDEWEB)
Bonham, H.B.
1976-08-01
Hybrid intraconnections join thin film networks and applique components into an electrically functional hybrid microcircuit (HMC). Applique components were intraconnected with thermocompression (TC) bonds to chromium/gold metallized thin film networks. The project determined critical processes, material parameters, quality criteria, and characterization techniques. The program began on July 1, 1970, and initial efforts consisted of organizing the development program and forecasting needed equipment and manpower. The total time schedule was then PERT-charted. Gold beam-lead devices, gold wire, and chromium/gold films had already been selected for these intraconnection technologies. Thermocompression bonding was developed to provide intraconnections between the HMC gold conductor metallization and gold beam-lead active devices, gold plated external leads, and gold terminated applique devices, such as capacitors. An optimum combination of bonding parameters based on the fundamental physics of the process was developed. This philosophy required bonding equipment to be thoroughly characterized so that the value of bonding parameters (such as force and temperature) indicated on the bonder could be translated into measurable values of the force and temperature within the bond zone.
Cellular signalling properties in microcircuits
DEFF Research Database (Denmark)
Toledo-Rodriguez, Maria; El Manira, Abdeljabbar; Wallén, Peter
2005-01-01
Molecules and cells are the signalling elements in microcircuits. Recent studies have uncovered bewildering diversity in postsynaptic signalling properties in all areas of the vertebrate nervous system. Major effort is now being invested in establishing the specialized signalling properties at th...
2017-01-01
Abstract Humans instantly recognize a previously seen face as “familiar.” To deepen our understanding of familiarity-novelty detection, we simulated biologically plausible neural network models of generic cortical microcircuits consisting of spiking neurons with random recurrent synaptic connections. NMDA receptor (NMDAR)-dependent synaptic plasticity was implemented to allow for unsupervised learning and bidirectional modifications. Network spiking activity evoked by sensory inputs consisting of face images altered synaptic efficacy, which resulted in the network responding more strongly to a previously seen face than a novel face. Network size determined how many faces could be accurately recognized as familiar. When the simulated model became sufficiently complex in structure, multiple familiarity traces could be retained in the same network by forming partially-overlapping subnetworks that differ slightly from each other, thereby resulting in a high storage capacity. Fisher’s discriminant analysis was applied to identify critical neurons whose spiking activity predicted familiar input patterns. Intriguingly, as sensory exposure was prolonged, the selected critical neurons tended to appear at deeper layers of the network model, suggesting recruitment of additional circuits in the network for incremental information storage. We conclude that generic cortical microcircuits with bidirectional synaptic plasticity have an intrinsic ability to detect familiar inputs. This ability does not require a specialized wiring diagram or supervision and can therefore be expected to emerge naturally in developing cortical circuits. PMID:28534043
Dense neuron clustering explains connectivity statistics in cortical microcircuits.
Directory of Open Access Journals (Sweden)
Vladimir V Klinshov
Full Text Available Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.
Hybrid Microcircuit Rework Procedures Evaluation.
1980-08-01
solder bonding of active device chips has been reported by Himmel .(8) The overall objective of this study was to determine which die bonding process steps...Tech., Sept. 1975, pp. 45-48. ()Hybrid Microcircuit In-Process Qualification, Final Report, Aug. 1976, Contract No. ECOM-75-1311-F, R. P. Himmel ...substrate bonding pad metallization. Table 4 lists the materials variables employed. Table 5 lists all the material combinations evaluated by Himmel
National Research Council Canada - National Science Library
Murray, John D; Anticevic, Alan; Gancsos, Mark; Ichinose, Megan; Corlett, Philip R; Krystal, John H; Wang, Xiao-Jing
2014-01-01
.... To elucidate the link between these phenomena, we incorporated synaptic disinhibition, via N-methyl-D-aspartate receptor perturbation on interneurons, into a network model of spatial working memory (WM...
Spike Neural Models Part II: Abstract Neural Models
Johnson, Melissa G.; Chartier, Sylvain
2018-01-01
Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN) though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF) model whic...
Decline in Radiation Hardened Microcircuit Infrastructure
LaBel, Kenneth A.
2015-01-01
Two areas of radiation hardened microcircuit infrastructure will be discussed: 1) The availability and performance of radiation hardened microcircuits, and, and 2) The access to radiation test facilities primarily for proton single event effects (SEE) testing. Other areas not discussed, but are a concern include: The challenge for maintaining radiation effects tool access for assurance purposes, and, the access to radiation test facilities primarily for heavy ion single event effects (SEE) testing. Status and implications will be discussed for each area.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Automatic visual inspection of hybrid microcircuits
Energy Technology Data Exchange (ETDEWEB)
Hines, R.E.
1980-05-01
An automatic visual inspection system using a minicomputer and a video digitizer was developed for inspecting hybrid microcircuits (HMC) and thin-film networks (TFN). The system performed well in detecting missing components on HMCs and reduced the testing time for each HMC by 75%.
Transient and steady-state selection in the striatal microcircuit
Directory of Open Access Journals (Sweden)
Adam eTomkins
2014-01-01
Full Text Available Although the basal ganglia have been widely studied and implicated in signal processing and action selection, little information is known about the active role the striatal microcircuit plays in action selection in the basal ganglia-thalamo-cortical loops. To address this knowledge gap we use a large scale three dimensional spiking model of the striatum, combined with a rate coded model of the basal ganglia-thalamo-cortical loop, to asses the computational role the striatum plays in action selection. We identify a robust transient phenomena generated by the striatal microcircuit, which temporarily enhances the difference between two competing cortical inputs. We show that this transient is sufficient to modulate decision making in the basal ganglia-thalamo-cortical circuit. We also find that the transient selection originates from a novel adaptation effect in single striatal projection neurons, which is amenable to experimental testing. Finally, we compared transient selection with models implementing classical steady-state selection. We challenged both forms of model to account for recent reports of paradoxically enhanced response selection in Huntington's Disease patients. We found that steady-state selection was uniformly impaired under all simulated Huntington's conditions, but transient selection was enhanced given a sufficient Huntington's-like increase in NMDA receptor sensitivity. Thus our models provide an intriguing hypothesis for the mechanisms underlying the paradoxical cognitive improvements in manifest Huntington's patients.
Schubert, D.; Kotter, R.; Staiger, J.F.
2007-01-01
Synaptic circuits bind together functional modules of the neocortex. We aim to clarify in a rodent model how intra- and transcolumnar microcircuits in the barrel cortex are laid out to segregate and also integrate sensory information. The primary somatosensory (barrel) cortex of rodents is the ideal
Sequential neural models with stochastic layers
DEFF Research Database (Denmark)
Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich
2016-01-01
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural ...... the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling....
Spiking modular neural networks: A neural network modeling approach for hydrological processes
National Research Council Canada - National Science Library
Kamban Parasuraman; Amin Elshorbagy; Sean K. Carey
2006-01-01
.... In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer...
Hybrid microcircuit via development. Final report
Energy Technology Data Exchange (ETDEWEB)
Norwood, D.P.
1980-06-01
Manufacturing processes were developed for making holes in alumina substrates, metallizing substrates on both sides and through the holes (vias), dry film photolithographing 6-..mu..m-thick gold to 127-..mu..m line widths and spacings, determining via quality, and protecting vias during HMC assembly processes. The few problems encountered were solved, and via technology is now established as a reliable and repeatable production technology for hybrid microcircuits. Via resistance on product HMCs usually measures from 4 to 6 m..cap omega.. which is well below the 10 m..cap omega.. maximum limit.
Information processing in micro and meso-scale neural circuits during normal and disease states
Luongo, Francisco
Neural computation can occur at multiple spatial and temporal timescales. The sum total of all of these processes is to guide optimal behaviors within the context of the constraints imposed by the physical world. How the circuits of the brain achieves this goal represents a central question in systems neuroscience. Here I explore the many ways in which the circuits of the brain can process information at both the micro and meso scale. Understanding the way information is represented and processed in the brain could shed light on the neuropathology underlying complex neuropsychiatric diseases such as autism and schizophrenia. Chapter 2 establishes an experimental paradigm for assaying patterns of microcircuit activity and examines the role of dopaminergic modulation on prefrontal microcircuits. We find that dopamine type 2 (D2) receptor activation results in an increase in spontaneous activity while dopamine type 1 (D1) activation does not. Chapter 3 of this dissertation presents a study that illustrates how cholingergic activation normally produces what has been suggested as a neural substrate of attention; pairwise decorrelation in microcircuit activity. This study also shows that in two etiologicall distinct mouse models of autism, FMR1 knockout mice and Valproic Acid exposed mice, this ability to decorrelate in the presence of cholinergic activation is lost. This represents a putative microcircuit level biomarker of autism. Chapter 4 examines the structure/function relationship within the prefrontal microcircuit. Spontaneous activity in prefrontal microcircuits is shown to be organized according to a small world architecture. Interestingly, this architecture is important for one concrete function of neuronal microcircuits; the ability to produce temporally stereotyped patterns of activation. In the final chapter, we identify subnetworks in chronic intracranial electrocorticographic (ECoG) recordings using pairwise electrode coherence and dimensionality reduction
Optimal Decision Making in Neural Inhibition Models
van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan
2012-01-01
In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…
Electromagnetic radiation screening of microcircuits for long life applications
Brammer, W. G.; Erickson, J. J.; Levy, M. E.
1974-01-01
The utility of X-rays as a stimulus for screening high reliability semiconductor microcircuits was studied. The theory of the interaction of X-rays with semiconductor materials and devices was considered. Experimental measurements of photovoltages, photocurrents, and effects on specified parameters were made on discrete devices and on microcircuits. The test specimens included discrete devices with certain types of identified flaws and symptoms of flaws, and microcircuits exhibiting deviant electrical behavior. With a necessarily limited sample of test specimens, no useful correlation could be found between the X-ray-induced electrical response and the known or suspected presence of flaws.
Neural networks as models of psychopathology.
Aakerlund, L; Hemmingsen, R
1998-04-01
Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
Comparison of metallization systems for thin film hybrid microcircuits
Energy Technology Data Exchange (ETDEWEB)
Hines, R.A.; Raut, M.K.
1980-08-01
Five metallization systems were evaluated for fabricating thin film hybrid microcircuits. The titanium/palladium/electroplated gold system proved superior in terms of thermocompression bondability, corrosion resistance, and solderability.
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK
Directory of Open Access Journals (Sweden)
R. Hadapiningradja Kusumodestoni
2015-11-01
Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.
Proceedings of the ECOM Hybrid Microcircuit Symposium, 1976
1976-06-01
Space Shuttle Avionics System S. Gaudiano, Lyndon B. Johnson Space Center, NASA 130 Hybrid Microcircuit Failure Modes R. P. Himmel , Hughes Aircraft...R. P. Himmel , A, Koudounaris and F. Z. Keister, "Evaluation of Techniques for Sealing Large Hybrid Packages," Final Technical Report, RADC-TR-76-17...REWORK EXPERIENCE 145 HYBRID MICROCIRCUIT FAILURE MODES R. P. Himmel Components and Materials Laboratories Hughes Aircraft Company Culver City
Pain processing by spinal microcircuits: afferent combinatorics.
Prescott, Steven A; Ratté, Stéphanie
2012-08-01
Pain, itch, heat, cold, and touch represent different percepts arising from somatosensory input. How stimuli give rise to these percepts has been debated for over a century. Recent work supports the view that primary afferents are highly specialized to transduce and encode specific stimulus modalities. However, cross-modal interactions (e.g. inhibition or exacerbation of pain by touch) support convergence rather than specificity in central circuits. We outline how peripheral specialization together with central convergence could enable spinal microcircuits to combine inputs from distinctly specialized, co-activated afferents and to modulate the output signals thus formed through computations like normalization. These issues will be discussed alongside recent advances in our understanding of microcircuitry in the superficial dorsal horn. Copyright © 2012 Elsevier Ltd. All rights reserved.
Empirical generalization assessment of neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1995-01-01
This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...
A quantum-implementable neural network model
Chen, Jialin; Wang, Lingli; Charbon, Edoardo
2017-10-01
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-11-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Artificial neural network cardiopulmonary modeling and diagnosis
Kangas, Lars J.; Keller, Paul E.
1997-01-01
The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
Neural modeling of prefrontal executive function
Energy Technology Data Exchange (ETDEWEB)
Levine, D.S. [Univ. of Texas, Arlington, TX (United States)
1996-12-31
Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.
A neural model of hierarchical reinforcement learning.
Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris
2017-01-01
We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.
A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION
Schomaker, Lambert
Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.
DEFF Research Database (Denmark)
Roudi, Yasser; Tyrcha, Joanna; Hertz, John
2009-01-01
(dansk abstrakt findes ikke) We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
Temporal-pattern learning in neural models
Genís, Carme Torras
1985-01-01
While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica tion of the pacemaker neuron model proposed together with its valida tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve ral factors r...
Microcircuit Modeling and Simulation beyond Ohm's Law
Saxena, T.; Chek, D. C. Y.; Tan, M. L. P.; Arora, V. K.
2011-01-01
Circuit theory textbooks rely heavily on the applicability of Ohm's law, which collapses as electronic components reach micro- and nanoscale dimensions. Circuit analysis is examined in the regime where the applied voltage V is greater than the critical voltage V[subscript c], which triggers the nonlinear behavior. The critical voltage is infinity…
Optimizing neural network models: motivation and case studies
Harp, S A; T. Samad
2012-01-01
Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally rem...
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural
Neural Network Program Package for Prosody Modeling
Directory of Open Access Journals (Sweden)
J. Santarius
2004-04-01
Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].
Flood routing modelling with Artificial Neural Networks
Directory of Open Access Journals (Sweden)
R. Peters
2006-01-01
Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.
A Computational Framework for Realistic Retina Modeling.
Martínez-Cañada, Pablo; Morillas, Christian; Pino, Begoña; Ros, Eduardo; Pelayo, Francisco
2016-11-01
Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.
Modeling Broadband Microwave Structures by Artificial Neural Networks
Directory of Open Access Journals (Sweden)
V. Otevrel
2004-06-01
Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.
Neural Network Model of memory retrieval
Directory of Open Access Journals (Sweden)
Stefano eRecanatesi
2015-12-01
Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.
Automated Modeling of Microwave Structures by Enhanced Neural Networks
Directory of Open Access Journals (Sweden)
Z. Raida
2006-12-01
Full Text Available The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. In the paper, neural networks are used to approximate the behavior of a planar microwave filter (moment method, Zeland IE3D. In order to evaluate the efficiency of neural modeling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and the accuracy. Considering conclusions, methodological recommendations for including neural networks to the microwave design are formulated.
A neural network model for texture discrimination.
Xing, J; Gerstein, G L
1993-01-01
A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.
Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex
DEFF Research Database (Denmark)
Fardo, Francesca; Auksztulewicz, Ryszard; Allen, Micah
2017-01-01
The neural processing and experience of pain are influenced by both expectations and attention. For example, the amplitude of event-related pain responses is enhanced by both novel and unexpected pain, and by moving the focus of attention towards a painful stimulus. Under predictive coding...... be mapped onto changes in effective connectivity between or within specific neuronal populations, using a canonical microcircuit (CMC) model of hierarchical processing. We thus implemented a CMC within dynamic causal modelling (DCM) for magnetoencephalography in human subjects, to investigate how...
Novel mathematical neural models for visual attention
DEFF Research Database (Denmark)
Li, Kang
Visual attention has been extensively studied in psychology, but some fundamental questions remain controversial. We focus on two questions in this study. First, we investigate how a neuron in visual cortex responds to multiple stimuli inside the receptive eld, described by either a response...... for the visual attention theories and spiking neuron models for single spike trains. Statistical inference and model selection are performed and various numerical methods are explored. The designed methods also give a framework for neural coding under visual attention theories. We conduct both analysis on real...... system, supported by simulation study. Finally, we present the decoding of multiple temporal stimuli under these visual attention theories, also in a realistic biophysical situation with simulations....
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2017-11-02
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Bio-Inspired Neural Model for Learning Dynamic Models
Duong, Tuan; Duong, Vu; Suri, Ronald
2009-01-01
A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.
Assessment of neural networks performance in modeling rainfall ...
African Journals Online (AJOL)
This paper presents the evaluation of performance of Neural Network (NN) model in predicting the behavioral pattern of rainfall depths of some locations in the North Central zones of Nigeria. The input to the model is the consecutive rainfall depths data obtained from the Nigerian Meteorological (NiMET) Agency. The neural ...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
MICHAEL
modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data obtained from an inverse fluidized bed reactor treating the starch industry wastewater.
A Bayesian framework for simultaneously modeling neural and behavioral data.
Turner, Brandon M; Forstmann, Birte U; Wagenmakers, Eric-Jan; Brown, Scott D; Sederberg, Per B; Steyvers, Mark
2013-05-15
Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice. Copyright © 2013 Elsevier Inc. All rights reserved.
A Bayesian framework for simultaneously modeling neural and behavioral data✩
Turner, Brandon M.; Forstmann, Birte U.; Wagenmakers, Eric-Jan; Brown, Scott D.; Sederberg, Per B.; Steyvers, Mark
2013-01-01
Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice. PMID:23370060
Hybrid microcircuit technology handbook materials, processes, design, testing and production
Licari, James J
1998-01-01
The Hybrid Microcircuit Technology Handbook integrates the many diverse technologies used in the design, fabrication, assembly, and testing of hybrid segments crucial to the success of producing reliable circuits in high yields. Among these are: resistor trimming, wire bonding, die attachment, cleaning, hermetic sealing, and moisture analysis. In addition to thin films, thick films, and assembly processes, important chapters on substrate selections, handling (including electrostatic discharge), failure analysis, and documentation are included. A comprehensive chapter of design guidelines will
Hybrid neural network bushing model for vehicle dynamics simulation
Energy Technology Data Exchange (ETDEWEB)
Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)
2008-12-15
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
Runoff Modelling in Urban Storm Drainage by Neural Networks
DEFF Research Database (Denmark)
Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld
1995-01-01
A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....
Ruzicka, W Brad; Subburaju, Sivan; Coyle, Joseph T; Benes, Francine M
2018-01-15
Recent studies describe distinct DNA methylomes among phenotypic subclasses of neurons in the human brain, but variation in DNA methylation between common neuronal phenotypes distinguished by their function within distinct neural circuits remains an unexplored concept. Studies able to resolve epigenetic profiles at the level of microcircuits are needed to illuminate chromatin dynamics in the regulation of specific neuronal populations and circuits mediating normal and abnormal behaviors. The Illumina HumanMethylation450 BeadChip was used to assess genome-wide DNA methylation in stratum oriens GABAergic interneurons sampled by laser-microdissection from two discrete microcircuits along the trisynaptic pathway in postmortem human hippocampus from eight control, eight schizophrenia, and eight bipolar disorder subjects. Data were analysed using the minfi Bioconductor package in R software version 3.3.2. We identified 11 highly significant differentially methylated regions associated with a group of genes with high construct-validity, including multiple zinc finger of the cerebellum gene family members and WNT signaling factors. Genomic locations of differentially methylated regions were highly similar between diagnostic categories, with a greater number of differentially methylated individual cytosine residues between circuit locations in bipolar disorder cases than in schizophrenia or control (42, 7, and 7 differentially methylated positions, respectively). These findings identify distinct DNA methylomes among phenotypically similar populations of GABAergic interneurons functioning within separate hippocampal subfields. These data compliment recent studies describing diverse epigenotypes among separate neuronal subclasses, extending this concept to distinct epigenotypes within similar neuronal phenotypes from separate microcircuits within the human brain. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email
Ocean wave prediction using numerical and neural network models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...
Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
Barrios, José Angel; Torres-Alvarado, Miguel; Cavazos, Alberto; Leduc, Luis
2011-10-01
In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.
Neural network models: Insights and prescriptions from practical applications
Energy Technology Data Exchange (ETDEWEB)
Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)
1995-12-31
Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.
A neural network model of causative actions.
Lee-Hand, Jeremy; Knott, Alistair
2015-01-01
A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz, 1997; Hommel et al., 2001; Sahin et al., 2007; Umiltà et al., 2008; Hommel, 2013). In this paper we extend existing models of effect-based action representations to account for a novel distinction. Some actions bring about effects that are independent events in their own right: for instance, if John smashes a cup, he brings about the event of the cup smashing. Other actions do not bring about such effects. For instance, if John grabs a cup, this action does not cause the cup to "do" anything: a grab action has well-defined perceptual effects, but these are not registered by the perceptual system that detects independent events involving external objects in the world. In our model, effect-based actions are implemented in several distinct neural circuits, which are organized into a hierarchy based on the complexity of their associated perceptual effects. The circuit at the top of this hierarchy is responsible for actions that bring about independently perceivable events. This circuit receives input from the perceptual module that recognizes arbitrary events taking place in the world, and learns movements that reliably cause such events. We assess our model against existing experimental observations about effect-based motor representations, and make some novel experimental predictions. We also consider the possibility that the "causative actions" circuit in our model can be identified with a motor pathway reported in other work, specializing in "functional" actions on manipulable tools (Bub et al., 2008; Binkofski and Buxbaum, 2013).
Computational modeling of neural plasticity for self-organization of neural networks.
Chrol-Cannon, Joseph; Jin, Yaochu
2014-11-01
Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Forecasting volatility with neural regression: a contribution to model adequacy.
Refenes, A N; Holt, W T
2001-01-01
Neural nets' usefulness for forecasting is limited by problems of overfitting and the lack of rigorous procedures for model identification, selection and adequacy testing. This paper describes a methodology for neural model misspecification testing. We introduce a generalization of the Durbin-Watson statistic for neural regression and discuss the general issues of misspecification testing using residual analysis. We derive a generalized influence matrix for neural estimators which enables us to evaluate the distribution of the statistic. We deploy Monte Carlo simulation to compare the power of the test for neural and linear regressors. While residual testing is not a sufficient condition for model adequacy, it is nevertheless a necessary condition to demonstrate that the model is a good approximation to the data generating process, particularly as neural-network estimation procedures are susceptible to partial convergence. The work is also an important step toward developing rigorous procedures for neural model identification, selection and adequacy testing which have started to appear in the literature. We demonstrate its applicability in the nontrivial problem of forecasting implied volatility innovations using high-frequency stock index options. Each step of the model building process is validated using statistical tests to verify variable significance and model adequacy with the results confirming the presence of nonlinear relationships in implied volatility innovations.
Spike neural models (part I: The Hodgkin-Huxley model
Directory of Open Access Journals (Sweden)
Johnson, Melissa G.
2017-05-01
Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.
Neural decision model of business capitalization
Directory of Open Access Journals (Sweden)
Martin Pokorný
2007-01-01
Full Text Available The topic of this article is focused on problems related to enterprise financial supervising. In the concrete, the situation of enterprise investment policy evaluation is described here. In this case, as a convenient tool for decision support, the approach of artificial intelligence was selected, particularly the model of neuron network. For the purpose of enterprise economic state evaluation, we use four input variables which describe the economic state. Three main variables are selected and the fourth one is the additional. The coding of main variables is chosen with the respect to the possible states of the enterprise. The multilayer neuron network was used for evaluation.The neural network can solve problems, which are hardly solvable for a manager because there can exist a lot of factors affecting the final decision. We have to take into account the fact that sometimes the situation is too complex. In this case, when the system gives incorrect result, it is possible to extend the current learning set and add adequate patterns which will help the system to recognize states of the enterprise.
Testing Neural Models of the Development of Infant Visual Attention
Richards, John E.; Hunter, Sharon K.
2002-01-01
Several models of the development of infant visual attention have used information about neural development. Most of these models have been based on nonhuman animal studies and have relied on indirect measures of neural development in human infants. This article discusses methods for studying a “neurodevelopmental” model of infant visual attention using indirect and direct measures of cortical activity. We concentrate on the effect of attention on eye movement control and show how animal-base...
Artificial neural network modeling of dissolved oxygen in reservoir.
Chen, Wei-Bo; Liu, Wen-Cheng
2014-02-01
The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.
Thick film fabrication of aluminum nitride microcircuits. Final report
Energy Technology Data Exchange (ETDEWEB)
Perdieu, L.H.
1994-03-01
A new substrate material, aluminum nitride (AlN), and 11 new thick film inks were analyzed to determine their chemical compatibility, their electrical properties, their mechanical properties, and their overall suitability for use in the manufacturing of high-power microcircuits with efficient thermal properties. Because high-power chips emit a great deal of heat in a small surface area, a new substrate material was needed to dissipate that heat faster than the substrate material currently in use. Overall, the new materials were found to be acceptable for accomplishing this purpose.
DEFF Research Database (Denmark)
Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin
2015-01-01
movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...
Small-signal neural models and their applications.
Basu, Arindam
2012-02-01
This paper introduces the use of the concept of small-signal analysis, commonly used in circuit design, for understanding neural models. We show that neural models, varying in complexity from Hodgkin-Huxley to integrate and fire have similar small-signal models when their corresponding differential equations are close to the same bifurcation with respect to input current. Three applications of small-signal neural models are shown. First, some of the properties of cortical neurons described by Izhikevich are explained intuitively through small-signal analysis. Second, we use small-signal models for deriving parameters for a simple neural model (such as resonate and fire) from a more complicated but biophysically relevant one like Morris-Lecar. We show similarity in the subthreshold behavior of the simple and complicated model when they are close to a Hopf bifurcation and a saddle-node bifurcation. Hence, this is useful to correctly tune simple neural models for large-scale cortical simulations. Finaly, the biasing regime of a silicon ion channel is derived by comparing its small-signal model with a Hodgkin-Huxley-type model.
Epigenomic Landscapes of hESC-Derived Neural Rosettes: Modeling Neural Tube Formation and Diseases.
Valensisi, Cristina; Andrus, Colin; Buckberry, Sam; Doni Jayavelu, Naresh; Lund, Riikka J; Lister, Ryan; Hawkins, R David
2017-08-08
We currently lack a comprehensive understanding of the mechanisms underlying neural tube formation and their contributions to neural tube defects (NTDs). Developing a model to study such a complex morphogenetic process, especially one that models human-specific aspects, is critical. Three-dimensional, human embryonic stem cell (hESC)-derived neural rosettes (NRs) provide a powerful resource for in vitro modeling of human neural tube formation. Epigenomic maps reveal enhancer elements unique to NRs relative to 2D systems. A master regulatory network illustrates that key NR properties are related to their epigenomic landscapes. We found that folate-associated DNA methylation changes were enriched within NR regulatory elements near genes involved in neural tube formation and metabolism. Our comprehensive regulatory maps offer insights into the mechanisms by which folate may prevent NTDs. Lastly, our distal regulatory maps provide a better understanding of the potential role of neurological-disorder-associated SNPs. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
Numerical analysis of modeling based on improved Elman neural network.
Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza
2014-01-01
A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.
Numerical Analysis of Modeling Based on Improved Elman Neural Network
Directory of Open Access Journals (Sweden)
Shao Jie
2014-01-01
Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.
Neural network models of learning and categorization in multigame experiments
Directory of Open Access Journals (Sweden)
Davide eMarchiori
2011-12-01
Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.
ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR
Directory of Open Access Journals (Sweden)
Martin Ruzek
2017-12-01
Full Text Available This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.
Neural Networks and Their Application to Air Force Personnel Modeling
1991-11-01
breadth of techniques provides fertile ground against which to compare the results obtained with neural networks. ", Most of the models in reenlistment or...Specialties (MOSs) receiving SRBs were taken from the 1980 and 1981 Enlisted Master Files ( EMFs ). These 98 MOSs were then aggregated into 15 Career Management... mechanisms , and architectures. Neural Networks, 1(1), 17-62. Hagiwara, M. (1990). Accelerated backpropagation using unlearning based on a Hebb rule
Stimulus-dependent maximum entropy models of neural population codes.
Directory of Open Access Journals (Sweden)
Einat Granot-Atedgi
Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.
Constitutive Modelling of INCONEL 718 using Artificial Neural Network
Abiriand Bhekisipho Twala, Olufunminiyi
2017-08-01
Artificial neural network is used to model INCONEL 718 in this paper. The model accounts for precipitate hardening in the alloy. The input variables for the neural network model are strain, strain rate, temperature and microstructure state. The output variable is the flow stress. The early stopping technique is combined with Bayesian regularization process in training the network. Sample and non-sample measurement data were taken from the literature. The model predictions of flow stress of the alloy are in good agreement with experimental measurements.
An ART neural network model of discrimination shift learning
Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.
2009-01-01
We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning
Modelling Framework of a Neural Object Recognition
Directory of Open Access Journals (Sweden)
Aswathy K S
2016-02-01
Full Text Available In many industrial, medical and scientific image processing applications, various feature and pattern recognition techniques are used to match specific features in an image with a known template. Despite the capabilities of these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating neural network techniques such image processing systems with much number of images can be trained until the system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.
Wei-Bo Chen; Wen-Cheng Liu
2015-01-01
In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical ...
Forecast of consumer behaviour based on neural networks models comparison
Directory of Open Access Journals (Sweden)
Michael Štencl
2012-01-01
Full Text Available The aim of this article is comparison of accuracy level of forecasted values of several artificial neural network models. The comparison is performed on datasets of Czech household consumption values. Several statistical models often resolve this task with more or fewer restrictions. In previous work where models’ input conditions were not so strict and model with missing data was used (the time series didn’t contain many values we have obtained comparably good results with artificial neural networks. Two views – practical and theoretical, motivate the purpose of this study. Forecasting models for medium term prognosis of the main trends of Czech household consumption is part of the faculty research design grant MSM 6215648904/03/02 (Sub-task 5.3 which defines the practical purpose. Testing of nonlinear autoregressive artificial neural network model compared with feed-forward neural network and radial basis function neural network defines the theoretical purpose. The performance metrics of the models were evaluated using a combination of common error metrics, namely Correlation Coefficient and Mean Square Error, together with the number of epochs and/or main prediction error.
Artificial neural networks modeling gene-environment interaction
Directory of Open Access Journals (Sweden)
Günther Frauke
2012-05-01
Full Text Available Abstract Background Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. Results In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. Conclusion Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.
Crabtree, Gregg W; Gogos, Joseph A
2014-01-01
Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations.
SCYNet. Testing supersymmetric models at the LHC with neural networks
Energy Technology Data Exchange (ETDEWEB)
Bechtle, Philip; Belkner, Sebastian; Hamer, Matthias [Universitaet Bonn, Bonn (Germany); Dercks, Daniel [Universitaet Hamburg, Hamburg (Germany); Keller, Tim; Kraemer, Michael; Sarrazin, Bjoern; Schuette-Engel, Jan; Tattersall, Jamie [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany)
2017-10-15
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. (orig.)
SCYNet: testing supersymmetric models at the LHC with neural networks
Bechtle, Philip; Belkner, Sebastian; Dercks, Daniel; Hamer, Matthias; Keller, Tim; Krämer, Michael; Sarrazin, Björn; Schütte-Engel, Jan; Tattersall, Jamie
2017-10-01
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.
2016-04-01
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Modelling of word usage frequency dynamics using artificial neural network
Maslennikova, Yu S.; Bochkarev, V. V.; Voloskov, D. S.
2014-03-01
In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models.
Technical Guidelines for LSI Hybrid Microcircuits.
1981-12-01
36E.- 0 1 08E"O 1 OE 1 o’. 1 0 : 3 1 0I .*0 : , f-., to . j 0 f5 --01 :1 o01 E +t 0 0 ’ 00)E ’I) Q 88.E -01 f? ;19E"-OI 20E3L 10 6 1. 011 1? 1 0 10...Two types of heat exchangers both of which are available off-the-shelf were investigated for use in the cold plate. One is the KINTEX Model No. 15000...the other a Wakefield No. 4434-2 extrusion. The KINTEX exchanger has superior performance and is recommended in this application. its core is
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...
An artificial neural network based fast radiative transfer model for ...
Indian Academy of Sciences (India)
the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of ... in construction, purpose and design and already in use are used. The fast RT model is able to ... porates measurements from various instruments in comparison with other ...
Discriminative training of self-structuring hidden control neural models
DEFF Research Database (Denmark)
Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben
1995-01-01
This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...
Artificial Neural Networks for Modeling Knowing and Learning in Science.
Roth, Wolff-Michael
2000-01-01
Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)
Role of neural network models for developing speech systems
Indian Academy of Sciences (India)
These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identiﬁcation. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...
Estimation of Effectivty Connectivity via Data-Driven Neural Modeling
Directory of Open Access Journals (Sweden)
Dean Robert Freestone
2014-11-01
Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.
The microcircuit concept applied to cortical evolution:from three-layer to six-layer cortex
Shepherd, Gordon M.
2011-01-01
Understanding the principles of organization of the cerebral cortex requires insight into its evolutionary history. This has traditionally been the province of anatomists, but evidence regarding the microcircuit organization of different cortical areas is providing new approaches to this problem. Here we use the microcircuit concept to focus first on the principles of microcircuit organization of three-layer cortex in the olfactory cortex, hippocampus and turtle general cortex, and compare...
On the nature, modeling, and neural bases of social ties.
van Winden, Frans; Stallen, Mirre; Ridderinkhof, K Richard
2008-01-01
This chapter addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual ('utility interdependence'). Ties can be positive or negative, and symmetric or asymmetric between individuals. Characteristic of a social tie, as conceived of here, is that it develops over time under the influence of interaction, in contrast with a trait like altruism. Moreover, a tie is not related to strategic behavior such as reputation formation but seen as generated by affective responses. A formalization is presented together with some supportive evidence from behavioral experiments. This is followed by a discussion of related psychological constructs and the presentation of suggestive existing neural findings. To help prepare the grounds for a model-based neural analysis some speculations on the neural networks involved are provided, together with suggestions for future research. Social ties are not only found to be important from an economic viewpoint, it is also shown that they can be modeled and related to neural substrates. By providing an overview of the economic research on social ties and connecting it with the broader behavioral and neuroeconomics literature, the chapter may contribute to the development of a neuroeconomics of social ties.
Modeling brain resonance phenomena using a neural mass model.
Directory of Open Access Journals (Sweden)
Andreas Spiegler
2011-12-01
Full Text Available Stimulation with rhythmic light flicker (photic driving plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.
Modeling of methane emissions using artificial neural network approach
Directory of Open Access Journals (Sweden)
Stamenković Lidija J.
2015-01-01
Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007
Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons
Rigatos, Gerasimos G
2015-01-01
This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.
Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations
Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus
2017-01-01
Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509
Aplication of artificial neural network model in aviation specialist training
Directory of Open Access Journals (Sweden)
Висиль Миколайович Казак
2016-02-01
Full Text Available This paper reviews the application of artificial neural network (ANN model in aviation specialist training. The ANN model is based on the dependence of residual knowledge of subjects of study on their individual abilities. The residual knowledge is the skills acquired by the subject before he is going for an occupation. The presented ANN model gives the possibility to predict the level of professional training of the specialists with high accuracy
THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE
Directory of Open Access Journals (Sweden)
António José Silva
2007-03-01
Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports
A hyperstable neural network for the modelling and control of ...
Indian Academy of Sciences (India)
A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other ...
A Constructive Neural-Network Approach to Modeling Psychological Development
Shultz, Thomas R.
2012-01-01
This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…
Introducing Artificial Neural Networks through a Spreadsheet Model
Rienzo, Thomas F.; Athappilly, Kuriakose K.
2012-01-01
Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…
Bilingual Lexical Interactions in an Unsupervised Neural Network Model
Zhao, Xiaowei; Li, Ping
2010-01-01
In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…
An artificial neural network based fast radiative transfer model for ...
Indian Academy of Sciences (India)
In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in ...
Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition
Caza, Gregory A.; Knott, Alistair
2012-01-01
The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as ... A multi- objective optimization of hard turning using neural network modelling and swarm intelligence ... being used in this study), and these activated values in turn become the starting signals for the next adjacent ...
A Neural Network Model for Dynamics Simulation | Bholoa ...
African Journals Online (AJOL)
University of Mauritius Research Journal. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 15, No 1 (2009) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. A Neural Network Model for Dynamics Simulation. Ajeevsing ...
Improved neural network modeling of inverse lens distortion
CSIR Research Space (South Africa)
De Villiers, JP
2011-04-01
Full Text Available Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by a RADAR. This paper extends recent work using neural networks as a compromise between...
Dynamic causal models of neural system dynamics: current state ...
Indian Academy of Sciences (India)
2006-09-28
Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...
A model of interval timing by neural integration
Simen, Patrick; Balci, Fuat; deSouza, Laura; Cohen, Jonathan D.; Holmes, Philip
2011-01-01
We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes; that correlations among them can be largely cancelled by balancing excitation and inhibition; that neural populations can act as integrators; and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule’s predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior. PMID:21697374
Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction
Directory of Open Access Journals (Sweden)
Ayodele Ariyo Adebiyi
2014-01-01
Full Text Available This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.
HIV lipodystrophy case definition using artificial neural network modelling
DEFF Research Database (Denmark)
Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew
2003-01-01
OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...
Product Cost Management Structures: a review and neural network modelling
Directory of Open Access Journals (Sweden)
P. Jha
2003-11-01
Full Text Available This paper reviews the growth of approaches in product costing and draws synergies with information management and resource planning systems, to investigate potential application of state of the art modelling techniques of neural networks. Increasing demands on costing systems to serve multiple decision-making objectives, have made it essential to use better techniques for analysis of available data. This need is highlighted in the paper. The approach of neural networks, which have several analogous facets to complement and aid the information demands of modern product costing, Enterprise Resource Planning (ERP structures and the dominant-computing environment (for information management in the object oriented paradigm form the domain for investigation. Simulated data is used in neural network applications across activities that consume resources and deliver products, to generate information for monitoring and control decisions. The results in application for feature extraction and variation detection and their implications are presented in the paper.
Neural network versus classical time series forecasting models
Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam
2017-05-01
Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.
Statistical modelling of neural networks in {gamma}-spectrometry applications
Energy Technology Data Exchange (ETDEWEB)
Vigneron, V.; Martinez, J.M. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. de Mecanique et de Technologie; Morel, J.; Lepy, M.C. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. des Applications et de la Metrologie des Rayonnements Ionisants
1995-12-31
Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio {sup 235} U/({sup 235} U + {sup 236} U + {sup 238} U). The usual method consider a limited number of {Gamma}-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K{sub {alpha}} X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium {sup 235} U and {sup 238} U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs.
Evolution of Neural Dynamics in an Ecological Model
Directory of Open Access Journals (Sweden)
Steven Williams
2017-07-01
Full Text Available What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.
A continuous-time neural model for sequential action.
Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard
2014-11-05
Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
A neural model of decision making
Larsen, Torben
2008-01-01
Background: A descriptive neuroeconomic model is aimed for relativity of the concept of economic man to empirical science.Method: A 4-level client-server-integrator model integrating the brain models of McLean and Luria is the general framework for the model of empirical findings.Results: Decision making relies on integration across brain levels of emotional intelligence (LU) and logico-matematico intelligence (RIA), respectively. The integrated decision making formula approaching zero by bot...
DEFF Research Database (Denmark)
Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin
2015-01-01
Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...
Building footprint extraction from digital surface models using neural networks
Davydova, Ksenia; Cui, Shiyong; Reinartz, Peter
2016-10-01
Two-dimensional building footprints are a basis for many applications: from cartography to three-dimensional building models generation. Although, many methodologies have been proposed for building footprint extraction, this topic remains an open research area. Neural networks are able to model the complex relationships between the multivariate input vector and the target vector. Based on these abilities we propose a methodology using neural networks and Markov Random Fields (MRF) for automatic building footprint extraction from normalized Digital Surface Model (nDSM) and satellite images within urban areas. The proposed approach has mainly two steps. In the first step, the unary terms are learned for the MRF energy function by a four-layer neural network. The neural network is learned on a large set of patches consisting of both nDSM and Normalized Difference Vegetation Index (NDVI). Then prediction is performed to calculate the unary terms that are used in the MRF. In the second step, the energy function is minimized using a maxflow algorithm, which leads to a binary building mask. The building extraction results are compared with available ground truth. The comparison illustrates the efficiency of the proposed algorithm which can extract approximately 80% of buildings from nDSM with high accuracy.
An integrated modelling framework for neural circuits with multiple neuromodulators.
Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt
2017-01-01
Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.
Modeling of surface dust concentrations using neural networks and kriging
Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.
2016-12-01
Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.
Recursive Bayesian recurrent neural networks for time-series modeling.
Mirikitani, Derrick T; Nikolaev, Nikolay
2010-02-01
This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.
Current approaches to model extracellular electrical neural microstimulation
Directory of Open Access Journals (Sweden)
Sébastien eJoucla
2014-02-01
Full Text Available Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include 1 the computation of the electrical potential field created by the stimulation in the tissue, and 2 the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron’s response in a cable equation framework (compartmentalized neuron models. Then, we present a whole finite element approach allows the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.
A scale-free neural network for modelling neurogenesis
Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.
2006-11-01
In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.
PEM Fuel Cell Modelling Using Artificial Neural Networks
Doumbia, Mamadou Lamine
2016-01-01
Fuel cells are electrochemical devices that convert the chemical energy of a reaction directly into dc electrical energy. Proton Exchange Membrane (PEM) fuel cell is a suitable alternative for both electrical transportation and stationary applications. In this article, an Artificial Neural Network (ANN) modelling approach of a PEM fuel cell is developed. This model describes the behaviour of PEM fuel cell voltage under both steady-state and transient conditions. Moreover, the prediction of th...
A neural click model for web search
Borisov, A.; Markov, I.; de Rijke, M.; Serdyukov, P.
2016-01-01
Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented
Internal models and neural computation in the vestibular system.
Green, Andrea M; Angelaki, Dora E
2010-01-01
The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks
Zhelavskaya, Irina S.; Shprits, Yuri Y.; Spasojević, Maria
2017-11-01
We present the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model - a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model-predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM-H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
A neural model of decision making
DEFF Research Database (Denmark)
Larsen, Torben
2008-01-01
a range of 4-7 Hz) [Sauseng et al, 2006]. See, elaborated description at L4. Thalamo-cortico integration The characteristic resting pattern of the brain is α-waves in the 8-12 Hz/s band which indicates an autogenic cortical reset originated in Thalamus (α%) [Hanslmayr et al, 2007]. The dynamics......Background: A descriptive neuroeconomic model is aimed for relativity of the concept of economic man to empirical science. Method: A 4-level client-server-integrator model integrating the brain models of McLean and Luria is the general framework for the model of empirical findings. Results......: Decision making relies on integration across brain levels of emotional intelligence (LU) and logico-matematico intelligence (RIA), respectively. The integrated decision making formula approaching zero by bottom-up emotional and frontoparietal-downward logico-matematico learning is: CONC := LU...
A neural network model of attention-modulated neurodynamics.
Gu, Yuqiao; Liljenström, Hans
2007-12-01
Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.
Hand Posture Prediction Using Neural Networks within a Biomechanical Model
Directory of Open Access Journals (Sweden)
Marta C. Mora
2012-10-01
Full Text Available This paper proposes the use of artificial neural networks (ANNs in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals. Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device.
Functional Modeling of Neural-Glia Interaction
DEFF Research Database (Denmark)
Postnov, D.E.; Brazhe, N.A.; Sosnovtseva, Olga
2012-01-01
Functional modeling is an approach that focuses on the representation of the qualitative dynamics of the individual components (e.g. cells) of a system and on the structure of the interaction network.......Functional modeling is an approach that focuses on the representation of the qualitative dynamics of the individual components (e.g. cells) of a system and on the structure of the interaction network....
Regional Computation of TEC Using a Neural Network Model
Leandro, R. F.; Santos, M. C.
2004-05-01
One of the main sources of errors of GPS measurements is the ionosphere refraction. As a dispersive medium, the ionosphere allow its influence to be computed by using dual frequency receivers. In the case of single frequency receivers it is necessary to use models that tell us how big the ionospheric refraction is. The GPS broadcast message carries parameters of this model, namely Klobuchar model. Dual frequency receivers allow to estimate the influence of ionosphere in the GPS signal by the computation of TEC (Total Electron Content) values, that have a direct relationship with the magnitude of the delay caused by the ionosphere. One alternative is to create a regional model based on a network of dual frequency receivers. In this case, the regional behaviour of ionosphere is modelled in a way that it is possible to estimate the TEC values into or near this region. This regional model can be based on polynomials, for example. In this work we will present a Neural Network-based model to the regional computation of TEC. The advantage of using a Neural Network is that it is not necessary to have a great knowledge on the behaviour of the modelled surface due to the adaptation capability of neural networks training process, that is an iterative adjust of the synaptic weights in function of residuals, using the training parameters. Therefore, the previous knowledge of the modelled phenomena is important to define what kind of and how many parameters are needed to train the neural network so that reasonable results are obtained from the estimations. We have used data from the GPS tracking network in Brazil, and we have tested the accuracy of the new model to all locations where there is a station, accessing the efficiency of the model everywhere. TEC values were computed for each station of the network. After that the training parameters data set for the test station was formed, with the TEC values of all others (all stations, except the test one). The Neural Network was
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
2016-01-01
When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet...
A Neural Computational Model of Incentive Salience
Zhang, Jun; Berridge, Kent C.; Tindell, Amy J.; Smith, Kyle S.; Aldridge, J. Wayne
2009-01-01
Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered ‘wanting’ only by
A Pruning Neural Network Model in Credit Classification Analysis
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Yajiao Tang
2018-01-01
Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.
A neural model of retrospective attention in visual working memory.
Bays, Paul M; Taylor, Robert
2018-02-01
An informative cue that directs attention to one of several items in working memory improves subsequent recall of that item. Here we examine the mechanism of this retro-cue effect using a model of short-term memory based on neural population coding. Our model describes recalled feature values as the output of an optimal decoding of spikes generated by a tuned population of neurons. This neural model provides a better account of human recall data than an influential model that assumes errors can be described as a mixture of normally distributed noise and random guesses. The retro-cue benefit is revealed to be consistent with a higher firing rate of the population encoding the cued versus uncued items, with no difference in tuning specificity. Additionally, a retro-cued item is less likely to be swapped with another item in memory, an effect that can also be explained by greater activity of the underlying population. These results provide a parsimonious account of the effects of retrospective attention on recall and demonstrate a principled method for investigating neural representations with behavioral tasks. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Selective modulation of excitatory and inhibitory microcircuits by dopamine
Gao, Wen-Jun; Goldman-Rakic, Patricia S.
2003-03-01
Dopamine plays an important role in the working memory functions of the prefrontal cortex, functions that are impacted in age-related memory decline, drug abuse, and a wide variety of disorders, including schizophrenia and Parkinson's disease. We have previously reported that dopamine depresses excitatory transmission between pyramidal neurons in the prefrontal cortex. Here, using paired recordings, we have investigated dopaminergic modulation of excitatory transmission from pyramidal neurons to fast-spiking (FS) interneurons. In contrast to its effect on recurrent excitation, dopamine was without effect on excitatory transmission to FS interneurons. However, dopamine has directly enhanced the excitability of the FS interneurons to the extent that even a single excitatory postsynaptic potential could initiate spiking with great temporal precision in some of them. These results indicate that dopamine's effects on excitatory transmission are target-specific and that the axon terminals of pyramidal neurons can be selectively regulated at the level of individual synapses. Thus, dopamine's net inhibitory effect on cortical function is remarkably constrained by the nature of the microcircuit elements on which it acts.
A Neural Network Model for Prediction of Sound Quality
DEFF Research Database (Denmark)
Nielsen,, Lars Bramsløw
An artificial neural network structure has been specified, implemented and optimized for the purpose of predicting the perceived sound quality for normal-hearing and hearing-impaired subjects. The network was implemented by means of commercially available software and optimized to predict results...... error on the test set. The overall concept proved functional, but further testing with data obtained from a new rating experiment is necessary to better assess the utility of this measure. The weights in the trained neural networks were analyzed to qualitatively interpret the relation between...... obtained in subjective sound quality rating experiments based on input data from an auditory model. Various types of input data and data representations from the auditory model were used as input data for the chosen network structure, which was a three-layer perceptron. This network was trained by means...
Role of SDF1/CXCR4 Interaction in Experimental Hemiplegic Models with Neural Cell Transplantation
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Noboru Suzuki
2012-02-01
Full Text Available Much attention has been focused on neural cell transplantation because of its promising clinical applications. We have reported that embryonic stem (ES cell derived neural stem/progenitor cell transplantation significantly improved motor functions in a hemiplegic mouse model. It is important to understand the molecular mechanisms governing neural regeneration of the damaged motor cortex after the transplantation. Recent investigations disclosed that chemokines participated in the regulation of migration and maturation of neural cell grafts. In this review, we summarize the involvement of inflammatory chemokines including stromal cell derived factor 1 (SDF1 in neural regeneration after ES cell derived neural stem/progenitor cell transplantation in mouse stroke models.
Neural system modeling and simulation using Hybrid Functional Petri Net.
Tang, Yin; Wang, Fei
2012-02-01
The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative ......-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron....
Capacitive MEMS accelerometer wide range modeling using artificial neural network
A. Baharodimehr; A. Abolfazl Suratgar; H. Sadeghi
2009-01-01
This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA). System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN) uses the Levenberg‐Marquardt (LM) method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation result...
Capacitive MEMS accelerometer wide range modeling using artificial neural network
Directory of Open Access Journals (Sweden)
A. Baharodimehr
2009-08-01
Full Text Available This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA. System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN uses the Levenberg‐Marquardt (LM method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation results are very promising.
Synthetic neural modeling applied to a real-world artifact.
Edelman, G M; Reeke, G N; Gall, W E; Tononi, G; Williams, D.; Sporns, O.
1992-01-01
We describe the general design, operating principles, and performance of a neurally organized, multiply adaptive device (NOMAD) under control of a nervous system simulated in a computer. The complete system, Darwin IV, is the latest in a series of models based on the theory of neuronal group selection, which postulates that adaptive behavior is the result of selection in somatic time among synaptic populations. The simulated brain of Darwin IV includes visual and motor areas that are connecte...
A model for the neural control of pineal periodicity
de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins
2016-12-01
The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.
Nonlinear modeling of neural population dynamics for hippocampal prostheses.
Song, Dong; Chan, Rosa H M; Marmarelis, Vasilis Z; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W
2009-11-01
Developing a neural prosthesis for the damaged hippocampus requires restoring the transformation of population neural activities performed by the hippocampal circuitry. To bypass a damaged region, output spike trains need to be predicted from the input spike trains and then reinstated through stimulation. We formulate a multiple-input, multiple-output (MIMO) nonlinear dynamic model for the input-output transformation of spike trains. In this approach, a MIMO model comprises a series of physiologically-plausible multiple-input, single-output (MISO) neuron models that consist of five components each: (1) feedforward Volterra kernels transforming the input spike trains into the synaptic potential, (2) a feedback kernel transforming the output spikes into the spike-triggered after-potential, (3) a noise term capturing the system uncertainty, (4) an adder generating the pre-threshold potential, and (5) a threshold function generating output spikes. It is shown that this model is equivalent to a generalized linear model with a probit link function. To reduce model complexity and avoid overfitting, statistical model selection and cross-validation methods are employed to choose the significant inputs and interactions between inputs. The model is applied successfully to the hippocampal CA3-CA1 population dynamics. Such a model can serve as a computational basis for the development of hippocampal prostheses.
Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
A dynamic neural field model of temporal order judgments.
Hecht, Lauren N; Spencer, John P; Vecera, Shaun P
2015-12-01
Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).
When Do Microcircuits Produce Beyond-Pairwise Correlations?
Directory of Open Access Journals (Sweden)
Andrea Katherine Barreiro
2014-02-01
Full Text Available Describing the collective activity of neural populations is a daunting task. Recent empirical studies in retina, however, suggest a vast simplification in how multi-neuron spiking occurs: the activity patterns of retinal ganglion cell populations under some conditions are nearly completely captured by pairwise interactions among neurons. In other circumstances, higher-order statistics are required and appear to be shaped by input statistics and intrinsic circuit mechanisms.Here, we study the emergence of higher-order interactions in a model of the retinal ganglion cell (RGC circuit in which correlations are generated by common input. We quantify the impact of higher-order interactions by comparing the responses of mechanistic circuit models vs. null descriptions in which all higher-than-pairwise correlations have been accounted for by lower order statistics; these are known as pairwise maximum entropy models. We find that over a broad range of stimuli, output spiking patterns are surprisingly well captured by the pairwise model.To understand this finding, we study an analytically tractable simplification of the RGC model. We find that in the simplified model, bimodal input signals produce larger deviations from pairwise predictions than unimodal inputs. The characteristic light filtering properties of the upstream RGC circuitry suppress bimodality in light stimuli, thus removing a powerful source of higher-order interactions. This provides a novel explanation for the surprising empirical success of pairwise models.
Prediction horizon effects on stochastic modelling hints for neural networks
Energy Technology Data Exchange (ETDEWEB)
Drossu, R.; Obradovic, Z. [Washington State Univ., Pullman, WA (United States)
1995-12-31
The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...
Validating neural-network refinements of nuclear mass models
Utama, R.; Piekarewicz, J.
2018-01-01
Background: Nuclear astrophysics centers on the role of nuclear physics in the cosmos. In particular, nuclear masses at the limits of stability are critical in the development of stellar structure and the origin of the elements. Purpose: We aim to test and validate the predictions of recently refined nuclear mass models against the newly published AME2016 compilation. Methods: The basic paradigm underlining the recently refined nuclear mass models is based on existing state-of-the-art models that are subsequently refined through the training of an artificial neural network. Bayesian inference is used to determine the parameters of the neural network so that statistical uncertainties are provided for all model predictions. Results: We observe a significant improvement in the Bayesian neural network (BNN) predictions relative to the corresponding "bare" models when compared to the nearly 50 new masses reported in the AME2016 compilation. Further, AME2016 estimates for the handful of impactful isotopes in the determination of r -process abundances are found to be in fairly good agreement with our theoretical predictions. Indeed, the BNN-improved Duflo-Zuker model predicts a root-mean-square deviation relative to experiment of σrms≃400 keV. Conclusions: Given the excellent performance of the BNN refinement in confronting the recently published AME2016 compilation, we are confident of its critical role in our quest for mass models of the highest quality. Moreover, as uncertainty quantification is at the core of the BNN approach, the improved mass models are in a unique position to identify those nuclei that will have the strongest impact in resolving some of the outstanding questions in nuclear astrophysics.
Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling
Aguilera, Miguel; Bedia, Manuel G.; Barandiaran, Xabier E.
2016-01-01
The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of “internalist neuroscience.” A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We
Artificial neural network modeling of p-cresol photodegradation.
Abdollahi, Yadollah; Zakaria, Azmi; Abbasiyannejad, Mina; Masoumi, Hamid Reza Fard; Moghaddam, Mansour Ghaffari; Matori, Khamirul Amin; Jahangirian, Hossein; Keshavarzi, Ashkan
2013-06-03
The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.
Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.
Directory of Open Access Journals (Sweden)
Gabriel Koch Ocker
2015-08-01
Full Text Available The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.
Neural Net Gains Estimation Based on an Equivalent Model
Directory of Open Access Journals (Sweden)
Karen Alicia Aguilar Cruz
2016-01-01
Full Text Available A model of an Equivalent Artificial Neural Net (EANN describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN. The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB the factors based on the functional error and the reference signal built with the past information of the system.
Computational models of the neural control of breathing.
Molkov, Yaroslav I; Rubin, Jonathan E; Rybak, Ilya A; Smith, Jeffrey C
2017-03-01
The ongoing process of breathing underlies the gas exchange essential for mammalian life. Each respiratory cycle ensues from the activity of rhythmic neural circuits in the brainstem, shaped by various modulatory signals, including mechanoreceptor feedback sensitive to lung inflation and chemoreceptor feedback dependent on gas composition in blood and tissues. This paper reviews a variety of computational models designed to reproduce experimental findings related to the neural control of breathing and generate predictions for future experimental testing. The review starts from the description of the core respiratory network in the brainstem, representing the central pattern generator (CPG) responsible for producing rhythmic respiratory activity, and progresses to encompass additional complexities needed to simulate different metabolic challenges, closed-loop feedback control including the lungs, and interactions between the respiratory and autonomic nervous systems. The integrated models considered in this review share a common framework including a distributed CPG core network responsible for generating the baseline three-phase pattern of rhythmic neural activity underlying normal breathing. WIREs Syst Biol Med 2017, 9:e1371. doi: 10.1002/wsbm.1371 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.
Fuzzy stochastic neural network model for structural system identification
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
2017-01-01
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
Neural network connectivity and response latency modelled by stochastic processes
DEFF Research Database (Denmark)
Tamborrino, Massimiliano
is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...
Neural network connectivity and response latency modelled by stochastic processes
DEFF Research Database (Denmark)
Tamborrino, Massimiliano
is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Directory of Open Access Journals (Sweden)
Tilo Schwalger
2017-04-01
Full Text Available Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.
Analysis of Neural-BOLD Coupling through Four Models of the Neural Metabolic Demand
Directory of Open Access Journals (Sweden)
Christopher W Tyler
2015-12-01
Full Text Available The coupling of the neuronal energetics to the blood-oxygen-level-dependent (BOLD response is still incompletely understood. To address this issue, we compared the fits of four plausible models of neurometabolic coupling dynamics to available data for simultaneous recordings of the local field potential (LFP and the local BOLD response recorded from monkey primary visual cortex over a wide range of stimulus durations. The four models of the metabolic demand driving the BOLD response were: direct coupling with the overall LFP; rectified coupling to the LFP; coupling with a slow adaptive component of the implied neural population response; and coupling with the non-adaptive intracellular input signal defined by the stimulus time course. Taking all stimulus durations into account, the results imply that the BOLD response is most closely coupled with metabolic demand derived from the intracellular input waveform, without significant influence from the adaptive transients and nonlinearities exhibited by the LFP waveform.
Neural network modeling of a dolphin's sonar discrimination capabilities
DEFF Research Database (Denmark)
Andersen, Lars Nonboe; René Rasmussen, A; Au, WWL
1994-01-01
The capability of an echo-locating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information of the echoes [W. W. L. Au, J. Acoust. Soc. Am. 95, 2728–2735 (1994)]. In this study, both time...... and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters...
Design of a neurally plausible model of fear learning
Directory of Open Access Journals (Sweden)
Franklin B. Krasne
2011-07-01
Full Text Available A neurally oriented conceptual and computational model of fear conditioning ("Fraidy Rat" or FRAT has been constructed that accounts for many aspects of delay and context conditioning. Conditioning and extinction are the result of neuromodulation-controlled LTP at synapses of thalamic, cortical, and hippocampal afferents on principal cells and inhibitory interneurons of lateral and basal amygdala. The phenomena accounted for by the model (and simulated by the computational version include conditioning, secondary reinforcement, blocking, the immediate shock deficit, extinction, renewal, and a range of empirically valid effects of pre- and post-training ablation or inactivation of hippocampus or amygdala nuclei.
Predicting the parameters of energy installations with laser ignition: Neural network models
Directory of Open Access Journals (Sweden)
Alexey A. Pastukhov
2015-06-01
Full Text Available This article considers the possibility of using artificial neural networks for predicting the parameters of the model energy installation with laser ignition. The main stages of creating a prognostic model based on an artificial neural network have been presented. Input data were analyzed by principal component method. The synthesized neural network was designed to predict the parameter value of the model in question. The artificial neural network was trained by a back-propagation algorithm. The efficiency of the artificial neural networks and their applicability to predicting parameter values of various rocket engine elements were demonstrated.
Optical Neural Network Models Applied To Logic Program Execution
Stormon, Charles D.
1988-05-01
Logic programming is being used extensively by Artificial Intelligence researchers to solve problems including natural language processing and expert systems. These languages, of which Prolog is the most widely used, promise to revolutionize software engineering, but much greater performance is needed. Researchers have demonstrated the applicability of neural network models to the solution of certain NP-complete problems, but these methods are not obviously applicable to the execution of logic programs. This paper outlines the use of neural networks in four aspects of the logic program execution cycle, and discusses results of a simulation of three of these. Four neural network functional units are described, called the substitution agent, the clause filter, the structure processor, and the heuristics generator, respectively. Simulation results suggest that the system described may provide several orders of magnitude improvement in execution speed for large logic programs. However, practical implementation of the proposed architecture will require the application of optical computing techniques due to the large number of neurons required, and the need for massive, adaptive connectivity.
A neural model of valuation and information virality.
Scholz, Christin; Baek, Elisa C; O'Donnell, Matthew Brook; Kim, Hyun Suk; Cappella, Joseph N; Falk, Emily B
2017-03-14
Information sharing is an integral part of human interaction that serves to build social relationships and affects attitudes and behaviors in individuals and large groups. We present a unifying neurocognitive framework of mechanisms underlying information sharing at scale (virality). We argue that expectations regarding self-related and social consequences of sharing (e.g., in the form of potential for self-enhancement or social approval) are integrated into a domain-general value signal that encodes the value of sharing a piece of information. This value signal translates into population-level virality. In two studies (n = 41 and 39 participants), we tested these hypotheses using functional neuroimaging. Neural activity in response to 80 New York Times articles was observed in theory-driven regions of interest associated with value, self, and social cognitions. This activity then was linked to objectively logged population-level data encompassing n = 117,611 internet shares of the articles. In both studies, activity in neural regions associated with self-related and social cognition was indirectly related to population-level sharing through increased neural activation in the brain's value system. Neural activity further predicted population-level outcomes over and above the variance explained by article characteristics and commonly used self-report measures of sharing intentions. This parsimonious framework may help advance theory, improve predictive models, and inform new approaches to effective intervention. More broadly, these data shed light on the core functions of sharing-to express ourselves in positive ways and to strengthen our social bonds.
Hybrid neural network model for the design of beam subjected to ...
Indian Academy of Sciences (India)
This paper demonstrates the applicability of Artiﬁcial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam ...
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
Continuous Online Sequence Learning with an Unsupervised Neural Network Model.
Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff
2016-09-14
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
A neural network model of ventriloquism effect and aftereffect.
Directory of Open Access Journals (Sweden)
Elisa Magosso
Full Text Available Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli. By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.
Landslide susceptibility analysis using an artificial neural network model
Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani
2007-10-01
This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.
Computational modeling of neural activities for statistical inference
Kolossa, Antonio
2016-01-01
This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .
Cortical microcircuit dynamics mediating Binocular Rivalry: The role of adaptation in inhibition
Directory of Open Access Journals (Sweden)
Panagiota eTheodoni
2011-11-01
Full Text Available Perceptual bistability arises when two conflicting interpretations of an ambiguous stimulus or images in binocular rivalry (BR compete for perceptual dominance. From a computational point of view competition models based on cross-inhibition and adaptation have shown that noise is a crucial force for rivalry and operates in balance with adaptation in order to explain the observed alternations in perception. In particular, noise-driven transitions and adaptation-driven oscillations define two dynamical regimes and the system operates near its boundary. In order to gain insights into the microcircuit dynamics mediating spontaneous perceptual alternations we used a reduced recurrent attractor-based biophysically realistic spiking network well known for working memory, attention and decision-making, where a spike-frequency adaptation mechanism is implemented to account for perceptual bistability. We, thus, derived a consistently reduced four-variable population rate model using mean-field techniques and tested it on BR data collected from human subjects. Our model accounts for experimental data parameters such as time dominance, coefficient of variation and gamma distribution. In addition, we show that our model also operates on the boundary between noise and adaptation and agrees with Levelt’s second revised and fourth propositions. These results show for the first time that a consistent reduction of a biophysically realistic spiking network of integrate and fire neurons with spike frequency adaptation could account for BR. Moreover, we demonstrate that BR can be explained only through the dynamics of the competing neuronal pools, without taking into account the adaptation of inhibitory interneurons..However, adaptation of interneurons affects the optimal parametric space of the system, by decreasing the overall adaptation necessary for the bifurcation to occur.
Woodward, Alexander; Froese, Tom; Ikegami, Takashi
2015-02-01
The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.
A hybrid neural network model for noisy data regression.
Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M
2004-04-01
A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
Modelling electric trains energy consumption using Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Martinez Fernandez, P.; Garcia Roman, C.; Insa Franco, R.
2016-07-01
Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways. (Author)
Chaotic Simulated Annealing by A Neural Network Model with Transient Chaos
Chen, L; Chen, Luonan; Aihara, Kazuyuki
1997-01-01
We propose a neural network model with transient chaos, or a transiently chaotic neural network (TCNN) as an approximation method for combinatorial optimization problem, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decreasing of a bifurcation parameter corresponding to the "temperature" in usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaoti...
Artificial Neural Network versus Linear Models Forecasting Doha Stock Market
Yousif, Adil; Elfaki, Faiz
2017-12-01
The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.
Applications of Wavelet Neural Network Model to Building Settlement Prediction: A Case Study
Directory of Open Access Journals (Sweden)
Qulin TAN
2014-04-01
Full Text Available Deformation monitoring is a significant work for engineering safety, which is performed throughout the entire process of engineering design, construction and operation. Based on the theoretic analysis of wavelet and neural network, we applied the improved BP neural network model, auxiliary wavelet neural network model and embedded wavelet neural network model to the settlement prediction in one practical engineering monitoring project with MATLAB software programming. The cumulative and the interval settlement was predicted and compared with measured data. The overall performances of the three models were analyzed and compared. The results show that the accuracies of two kinds of wavelet neural network models are roughly the same, which prediction errors of monitoring points are less than 1mm, obviously superior to the single BP neural network model.
neural network based model o work based model of an industrial oil
African Journals Online (AJOL)
eobe
ropagation Algorithm (trainlm). The data; shows that data; shows that the simulated model the simulated model t outputs t outputs, with regression val with regression val with regression value of d to control the neural network model. , PID controller. ing processes. An additional node with ut (usually 1) is often added to the.
Hierarchical Neural Regression Models for Customer Churn Prediction
Directory of Open Access Journals (Sweden)
Golshan Mohammadi
2013-01-01
Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
Neural field model of memory-guided search
Kilpatrick, Zachary P.; Poll, Daniel B.
2017-12-01
Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing redundancies in the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.
Reconstructing neural dynamics using data assimilation with multiple models
Hamilton, Franz; Cressman, John; Peixoto, Nathalia; Sauer, Timothy
2014-09-01
Assimilation of data with models of physical processes is a critical component of modern scientific analysis. In recent years, nonlinear versions of Kalman filtering have been developed, in addition to methods that estimate model parameters in parallel with the system state. We propose a substantial extension of these tools to deal with the specific case of unmodeled variables, when training data from the variable is avaiable. The method uses a stack of several, nonidentical copies of a physical model to jointly reconstruct the variable in question. We demonstrate the ability of this technique to accurately recover an unmodeled experimental quantity, such as an ion concentration, from a single voltage trace after the training period is completed. The method is applied to reconstruct the potassium concentration in a neural culture from multielectrode array voltage measurements.
An effective convolutional neural network model for Chinese sentiment analysis
Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong
2017-06-01
Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.
Models of neural networks IV early vision and attention
Cowan, Jack; Domany, Eytan
2002-01-01
Close this book for a moment and look around you. You scan the scene by directing your attention, and gaze, at certain specific objects. Despite the background, you discern them. The process is partially intentional and partially preattentive. How all this can be done is described in the fourth volume of Models of Neural Networks devoted to Early Vision and Atten tion that you are holding in your hands. Early vision comprises the first stages of visual information processing. It is as such a scientific challenge whose clarification calls for a penetrating review. Here you see the result. The Heraeus Foundation (Hanau) is to be thanked for its support during the initial phase of this project. John Hertz, who has extensive experience in both computational and ex perimental neuroscience, provides in "Neurons, Networks, and Cognition" to neural modeling. John Van Opstal explains in a theoretical introduction "The Gaze Control System" how the eye's gaze control is performed and presents a novel theoretical des...
Synthetic neural modeling applied to a real-world artifact.
Edelman, G M; Reeke, G N; Gall, W E; Tononi, G; Williams, D; Sporns, O
1992-08-01
We describe the general design, operating principles, and performance of a neurally organized, multiply adaptive device (NOMAD) under control of a nervous system simulated in a computer. The complete system, Darwin IV, is the latest in a series of models based on the theory of neuronal group selection, which postulates that adaptive behavior is the result of selection in somatic time among synaptic populations. The simulated brain of Darwin IV includes visual and motor areas that are connected with NOMAD by telemetry. Under suitable conditions, Darwin IV can be trained to track a light moving in a random path. After such training, it can approach colored blocks and collect them to a home position. Following a series of contacts with such blocks, value signals received through a "snout" that senses conductivity allow it to sort these blocks on the basis of differences in color associated with differences in their conductivity. Darwin IV represents a new approach to synthetic neural modeling (SNM), a technique in which large-scale computer simulations are employed to analyze the interactions among the nervous system, the phenotype, and the environment of a designed organism as behavior develops. Darwin IV retains the advantages of SNM while avoiding the difficulties and pitfalls of attempting to simulate a rich environment in addition to a brain.
Models of Hopfield-type quaternion neural networks and their energy functions.
Yoshida, Mitsuo; Kuroe, Yasuaki; Mori, Takehiro
2005-01-01
Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. Furthermore models of neural networks that can deal with quaternion numbers, which is the extension of complex numbers, have also been proposed. However they are all multilayer quaternion neural networks. This paper proposes models of fully connected recurrent quaternion neural networks, Hopfield-type quaternion neural networks. Since quaternion numbers are non-commutative on multiplication, some different models can be considered. We investigate dynamics of these proposed models from the point of view of the existence of an energy function and derive their conditions for existence.
Acquiring neural signals for developing a perception and cognition model
Li, Wei; Li, Yunyi; Chen, Genshe; Shen, Dan; Blasch, Erik; Pham, Khanh; Lynch, Robert
2012-06-01
The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.
The Artifical Neural Network as means for modeling Nonlinear Systems
Drábek Oldøich; Taufer Ivan
1998-01-01
The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.
The Artifical Neural Network as means for modeling Nonlinear Systems
Directory of Open Access Journals (Sweden)
Drábek Oldøich
1998-12-01
Full Text Available The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Directory of Open Access Journals (Sweden)
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems
Ranganayaki, V.; Deepa, S. N.
2016-01-01
Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973
An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.
Ranganayaki, V; Deepa, S N
2016-01-01
Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.
Directory of Open Access Journals (Sweden)
Wei-Bo Chen
2015-01-01
Full Text Available In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS and a multilinear regression (MLR model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.
Identification of non-linear models of neural activity in bold fmri
DEFF Research Database (Denmark)
Jacobsen, Daniel Jakup; Madsen, Kristoffer Hougaard; Hansen, Lars Kai
2006-01-01
Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These ...
Artificial neural network modeling of plasmonic transmission lines.
Andrawis, Robert R; Swillam, Mohamed A; El-Gamal, Mohamed A; Soliman, Ezzeldin A
2016-04-01
In this paper, new models based on an artificial neural network (ANN) are developed to predict the propagation characteristics of plasmonic nanostrip and coupled nanostrips transmission lines. The trained ANNs are capable of providing the required propagation characteristics with good accuracy and almost instantaneously. The nonlinear mapping performed by the trained ANNs is written as closed-form expressions, which facilitate the direct use of the results obtained in this research. The propagation characteristics of the investigated transmission lines include the effective refractive index and the characteristic impedance. The time needed to simulate 1000 different versions of the transmission line structure is about 48 h, using a full-wave electromagnetic solver compared to 3 s using the developed ANN model.
The microcircuit concept applied to cortical evolution:from three-layer to six-layer cortex
Directory of Open Access Journals (Sweden)
Gordon M Shepherd
2011-05-01
Full Text Available Understanding the principles of organization of the cerebral cortex requires insight into its evolutionary history. This has traditionally been the province of anatomists, but evidence regarding the microcircuit organization of different cortical areas is providing new approaches to this problem. Here we use the microcircuit concept to focus first on the principles of microcircuit organization of three-layer cortex in the olfactory cortex, hippocampus and turtle general cortex, and compare it with six-layer neocortex. From this perspective it is possible to identify basic circuit elements for recurrent excitation and lateral inhibition that are common across all the cortical regions. Special properties of the apical dendrites of pyramidal cells are reviewed that reflect the specific adaptations that characterize the functional operations in the different regions. These principles of microcircuit organization provide a new approach to understanding the expanded functional capabilities elaborated by the evolution of the neocortex.
A low-order model of biological neural networks.
Lo, James Ting-Ho
2011-10-01
A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
A model of traffic signs recognition with convolutional neural network
Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing
2016-10-01
In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.
Improved estimation of energy expenditure by artificial neural network modeling.
Hay, Dean Charles; Wakayama, Akinobu; Sakamura, Ken; Fukashiro, Senshi
2008-12-01
Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.
Evaluation of an artificial neural network rainfall disaggregation model.
Burian, S J; Durran, S R
2002-01-01
Previous research produced an artificial neural network (ANN) temporal rainfall disaggregation model. After proper training the model can disaggregate hourly rainfall records into sub-hourly time increments. In this paper we present results from continued evaluations of the performance of the ANN model specifically examining how the errors in the disaggregated rainfall hyetograph translate to errors in the prediction of the runoff hydrograph. Using a rainfall-runoff model of a hypothetical watershed we compare the runoff hydrographs produced by the ANN-predicted 15-minute increment rainfall pattern to runoff hydrographs produced by (1) the observed 15-minute increment rainfall pattern, (2) the observed hourly-increment rainfall pattern, and (3) the 15-minute increment rainfall pattern produced by a disaggregation model based on geometric similarity. For 98 test storms the peak discharges produced by the ANN model rainfall pattern had a median under-prediction of 16.6%. This relative error was less than the median under-prediction in peak discharge when using the observed 15-minute rainfall patterns aggregated to hourly increments (40.8%), and when using rainfall patterns produced by the geometric similarity rainfall disaggregation model (21.9%).
Energy Technology Data Exchange (ETDEWEB)
Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)
2005-10-01
In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)
Ground Motion Prediction Model Using Artificial Neural Network
Dhanya, J.; Raghukanth, S. T. G.
2017-12-01
This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude (M w), closest distance to rupture plane (R rup), shear wave velocity in the region (V s30) and focal mechanism (F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.
Taming Many-Parameter BSM Models with Bayesian Neural Networks
Kuchera, M. P.; Karbo, A.; Prosper, H. B.; Sanchez, A.; Taylor, J. Z.
2017-09-01
The search for physics Beyond the Standard Model (BSM) is a major focus of large-scale high energy physics experiments. One method is to look for specific deviations from the Standard Model that are predicted by BSM models. In cases where the model has a large number of free parameters, standard search methods become intractable due to computation time. This talk presents results using Bayesian Neural Networks, a supervised machine learning method, to enable the study of higher-dimensional models. The popular phenomenological Minimal Supersymmetric Standard Model was studied as an example of the feasibility and usefulness of this method. Graphics Processing Units (GPUs) are used to expedite the calculations. Cross-section predictions for 13 TeV proton collisions will be presented. My participation in the Conference Experience for Undergraduates (CEU) in 2004-2006 exposed me to the national and global significance of cutting-edge research. At the 2005 CEU, I presented work from the previous summer's SULI internship at Lawrence Berkeley Laboratory, where I learned to program while working on the Majorana Project. That work inspired me to follow a similar research path, which led me to my current work on computational methods applied to BSM physics.
Directory of Open Access Journals (Sweden)
Taimoor Khan
2014-01-01
Full Text Available In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA- based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.
Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network
National Research Council Canada - National Science Library
Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu
2006-01-01
.... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...
Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Reza Pirmoradi
2012-04-01
Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.
ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.
Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan
2017-07-20
Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.
Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.
Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel
2016-02-01
Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.
Artificial neural network model for earthquake prediction with radon monitoring
Energy Technology Data Exchange (ETDEWEB)
Kuelahci, Fatih [Science and Art Faculty, Physics Department, Firat University, Elazig 23169 (Turkey)], E-mail: fatihkulahci@firat.edu.tr; Inceoez, Murat [Engineering Faculty, Geology Department, Firat University, Elazig 23169 (Turkey); Dogru, Mahmut [Science and Art Faculty, Physics Department, Firat University, Elazig 23169 (Turkey)], E-mail: mdogru@firat.edu.tr; Aksoy, Ercan [Engineering Faculty, Geology Department, Firat University, Elazig 23169 (Turkey); Baykara, Oktay [Education Faculty, Science Education Division, Firat University, Elazig 23169 (Turkey)
2009-01-15
Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations.
Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce
Directory of Open Access Journals (Sweden)
Wei-Chin Lin
2009-04-01
Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.
Neural modeling of bromelain extraction by reversed micelles
Directory of Open Access Journals (Sweden)
Ana Maria Frattini Fileti
2010-04-01
Full Text Available A pulsed-cap microcolumn was used for bromelain extraction from pineapple juice by reversed micelles. The cationic micellar solution used BDBAC as the surfactant, isooctane as the solvent and hexanol as the co-solvent. In order to capture the dynamic behavior and the nonlinearities of the column, the operating conditions were modified in accordance with the central composite design for the experiment, using the ratio between the light phase flow rate and the total flow rate, and the time interval between pulses. The effects on the purification factor and on total protein yield were modeled via neural networks. The best topology was defined as 16-9-2, and the input layer was a moving window of the independent variables. The neural model successfully predicted both the purification factor and the total protein yield from historical data. At the optimal operating point, a purification factor of 4.96 and a productivity of 1.29 mL/min were obtained.Uma micro-coluna com campânulas pulsantes foi utilizada para a extração de bromelina a partir de suco de abacaxi, usando micelas reversas. A solução catiônica micelar foi composta do surfactante BDBAC, do solvente iso-octano e do co-solvente hexanol. Seguindo um planejamento experimental, perturbações foram impostas à coluna de extração com o objetivo de capturar seu comportamento dinâmico e suas não-linearidades, usando a razão entre a vazão da fase leve e vazão total, e o intervalo de tempo entre os pulsos. Os efeitos das variáveis independentes sobre o fator de purificação e sobre o rendimento em proteínas totais foram modelados via redes neurais artificiais. A melhor topologia de rede obtida foi definida como 16-9-2, usando um esquema de janela móvel no tempo das variáveis independentes. O modelo neural obtido do histórico do processo se mostrou adequado para predizer simultaneamente o fator de purificação e o rendimento do processo em proteínas totais. No ponto ótimo de
Wavelet Neural Network Model for Yield Spread Forecasting
Directory of Open Access Journals (Sweden)
Firdous Ahmad Shah
2017-11-01
Full Text Available In this study, a hybrid method based on coupling discrete wavelet transforms (DWTs and artificial neural network (ANN for yield spread forecasting is proposed. The discrete wavelet transform (DWT using five different wavelet families is applied to decompose the five different yield spreads constructed at shorter end, longer end, and policy relevant area of the yield curve to eliminate noise from them. The wavelet coefficients are then used as inputs into Levenberg-Marquardt (LM ANN models to forecast the predictive power of each of these spreads for output growth. We find that the yield spreads constructed at the shorter end and policy relevant areas of the yield curve have a better predictive power to forecast the output growth, whereas the yield spreads, which are constructed at the longer end of the yield curve do not seem to have predictive information for output growth. These results provide the robustness to the earlier results.
Modelling the growth of Leuconostoc mesenteroides by Artificial Neural Networks.
García-Gimeno, R M; Hervás-Martínez, C; Rodríguez-Pérez, R; Zurera-Cosano, G
2005-12-15
The combined effect of temperature (10.5 to 24.5 degrees C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the predicted specific growth rate (Gr), lag-time (Lag) and maximum population density (yEnd) of Leuconostoc mesenteroides under aerobic and anaerobic conditions, was studied using an Artificial Neural Network-based model (ANN) in comparison with Response Surface Methodology (RS). For both aerobic and anaerobic conditions, two types of ANN model were elaborated, unidimensional for each of the growth parameters, and multidimensional in which the three parameters Gr, Lag, and yEnd are combined. Although in general no significant statistical differences were observed between both types of model, we opted for the unidimensional model, because it obtained the lowest mean value for the standard error of prediction for generalisation. The ANN models developed provided reliable estimates for the three kinetic parameters studied; the SEP values in aerobic conditions ranged from between 2.82% for Gr, 6.05% for Lag and 10% for yEnd, a higher degree accuracy than those of the RS model (Gr: 9.54%; Lag: 8.89%; yEnd: 10.27%). Similar results were observed for anaerobic conditions. During external validation, a higher degree of accuracy (Af) and bias (Bf) were observed for the ANN model compared with the RS model. ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters.
Crider, Krista S; Devine, Owen; Hao, Ling; Dowling, Nicole F; Li, Song; Molloy, Anne M; Li, Zhu; Zhu, Jianghui; Berry, Robert J
2014-07-29
To determine an optimal population red blood cell (RBC) folate concentration for the prevention of neural tube birth defects. Bayesian model. Data from two population based studies in China. 247,831 participants in a prospective community intervention project in China (1993-95) to prevent neural tube defects with 400 μg/day folic acid supplementation and 1194 participants in a population based randomized trial (2003-05) to evaluate the effect of folic acid supplementation on blood folate concentration among Chinese women of reproductive age. Folic acid supplementation (400 μg/day). Estimated RBC folate concentration at time of neural tube closure (day 28 of gestation) and risk of neural tube defects. Risk of neural tube defects was high at the lowest estimated RBC folate concentrations (for example, 25.4 (95% uncertainty interval 20.8 to 31.2) neural tube defects per 10,000 births at 500 nmol/L) and decreased as estimated RBC folate concentration increased. Risk of neural tube defects was substantially attenuated at estimated RBC folate concentrations above about 1000 nmol/L (for example, 6 neural tube defects per 10,000 births at 1180 (1050 to 1340) nmol/L). The modeled dose-response relation was consistent with the existing literature. In addition, neural tube defect risk estimates developed using the proposed model and population level RBC information were consistent with the prevalence of neural tube defects in the US population before and after food fortification with folic acid. A threshold for "optimal" population RBC folate concentration for the prevention of neural tube defects could be defined (for example, approximately 1000 nmol/L). Population based RBC folate concentrations, as a biomarker for risk of neural tube defects, can be used to facilitate evaluation of prevention programs as well as to identify subpopulations at elevated risk for a neural tube defect affected pregnancy due to folate insufficiency. © Crider et al 2014.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.
2016-01-01
We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Analysis of the developing neural system using an in vitro model by Raman spectroscopy.
Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi
2015-04-07
We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.
Pusuluri, Sai Teja
Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features
Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks
Wang, Fang; Kong, Deren; Yang, Lixia; Zhang, Zouzou
2017-08-01
Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.
Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling
Bakanovskaya, L. N.
2016-08-01
The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.
Energy Technology Data Exchange (ETDEWEB)
Zawicki, L.R.; Thomas, N.C.
1980-06-01
The development, testing, and uses of an edge monitor for monitoring the characteristics of thin metallic films used in the fabrication of hybrid microcircuits are described. The data obtained on this quality control equipment demonstrated improved techniques for monitoring thin film characteristics for reliable hybrid microcircuit fabrication. This was accomplished through the design of a specialized circuit, an integral part of the product substrate to be tested that enhances the effects of degradation mechanisms, both mechanically and electrically. Correlation of mechanical and/or physical characteristcs with electrical data was demonstrated. When coupled with a minicomputer test set with controlling and data retention capabilities, the system offers a useful tool for production process monitoring, diagnostic evaluation, or developmental efforts. (LCL)
Bildirici, Melike; Ersin, Özgür
2014-01-01
The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.
Directory of Open Access Journals (Sweden)
Melike Bildirici
2014-01-01
Full Text Available The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100. Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.
Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan
2018-02-01
Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.
Artificial neural network model of pork meat cubes osmotic dehydratation
Directory of Open Access Journals (Sweden)
Pezo Lato L.
2013-01-01
Full Text Available Mass transfer of pork meat cubes (M. triceps brachii, shaped as 1x1x1 cm, during osmotic dehydration (OD and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w, temperature (20-50ºC, and immersion time (1-5 h in terms of water loss (WL, solid gain (SG, final dry matter content (DM, and water activity (aw, were investigated using experimental results. Five artificial neural network (ANN models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.
Simple Electromagnetic Modeling of Small Airplanes: Neural Network Approach
Directory of Open Access Journals (Sweden)
P. Tobola
2009-04-01
Full Text Available The paper deals with the development of simple electromagnetic models of small airplanes, which can contain composite materials in their construction. Electromagnetic waves can penetrate through the surface of the aircraft due to the specific electromagnetic properties of the composite materials, which can increase the intensity of fields inside the airplane and can negatively influence the functionality of the sensitive avionics. The airplane is simulated by two parallel dielectric layers (the left-hand side wall and the right-hand side wall of the airplane. The layers are put into a rectangular metallic waveguide terminated by the absorber in order to simulate the illumination of the airplane by the external wave (both of the harmonic nature and pulse one. Thanks to the simplicity of the model, the parametric analysis can be performed, and the results can be used in order to train an artificial neural network. The trained networks excel in further reduction of CPU-time demands of an airplane modeling.
Neural network model to control an experimental chaotic pendulum
Bakker, R; Schouten, JC; Takens, F; vandenBleek, CM
1996-01-01
A feedforward neural network was trained to predict the motion of an experimental, driven, and damped pendulum operating in a chaotic regime. The network learned the behavior of the pendulum from a time series of the pendulum's angle, the single measured variable. The validity of the neural
Commentary. Integrative Modeling and the Role of Neural Constraints
Czech Academy of Sciences Publication Activity Database
Bantegnie, Brice
2017-01-01
Roč. 8, SEP 5 (2017), s. 1-2, č. článku 1531. ISSN 1664-1078 Institutional support: RVO:67985955 Keywords : mechanistic explanation * functional analysis * mechanistic integration * reverse inference * neural plasticity * neural networks Subject RIV: AA - Philosophy ; Religion Impact factor: 2.323, year: 2016
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high ...
Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.
2008-09-01
The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...
PREDICTION OF FINANCIAL FAILURE OF BANKS BY ARTIFICAL NEURAL NETWORK MODEL
National Research Council Canada - National Science Library
Utku ALTUNÖZ
2013-01-01
In this article financial failure prediction models based on artificial neural network model, which is among the multivariable statistical techniques has been tested in a parallel with literature surveys...
SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.
Jimenez-Romero, Cristian; Johnson, Jeffrey
2017-01-01
The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.
DEFF Research Database (Denmark)
Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin
2013-01-01
Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate......, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...
Stability of a neural predictive controller scheme on a neural model
DEFF Research Database (Denmark)
Luther, Jim Benjamin; Sørensen, Paul Haase
2009-01-01
In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system....
Validation of protein models by a neural network approach
Directory of Open Access Journals (Sweden)
Fantucci Piercarlo
2008-01-01
Full Text Available Abstract Background The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts.
Analytic Modeling of Neural Tissue: I. A Spherical Bidomain.
Schwartz, Benjamin L; Chauhan, Munish; Sadleir, Rosalind J
2016-12-01
Presented here is a model of neural tissue in a conductive medium stimulated by externally injected currents. The tissue is described as a conductively isotropic bidomain, i.e. comprised of intra and extracellular regions that occupy the same space, as well as the membrane that divides them, and the injection currents are described as a pair of source and sink points. The problem is solved in three spatial dimensions and defined in spherical coordinates [Formula: see text]. The system of coupled partial differential equations is solved by recasting the problem to be in terms of the membrane and a monodomain, interpreted as a weighted average of the intra and extracellular domains. The membrane and monodomain are defined by the scalar Helmholtz and Laplace equations, respectively, which are both separable in spherical coordinates. Product solutions are thus assumed and given through certain transcendental functions. From these electrical potentials, analytic expressions for current density are derived and from those fields the magnetic flux density is calculated. Numerical examples are considered wherein the interstitial conductivity is varied, as well as the limiting case of the problem simplifying to two dimensions due to azimuthal independence. Finally, future modeling work is discussed.
Neural Networks Modelling of Municipal Real Estate Market Rent Rates
Directory of Open Access Journals (Sweden)
Muczyński Andrzej
2016-12-01
Full Text Available This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.
Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR
Directory of Open Access Journals (Sweden)
Bernard P. Zeigler
2017-01-01
Full Text Available In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed of neuron-inspired components. In contrast to earlier generations of neural net models, third generation spiking neural nets exhibit important temporal and dynamic properties, and random neural nets provide alternative probabilistic approaches. Our definitions of realization are based on the Discrete Event System Specification (DEVS formalism that fundamentally include temporal and probabilistic characteristics of neuron system inputs, state, and outputs. The realizations that we construct—in particular for the Exclusive Or (XOR logic gate—provide insight into the temporal and probabilistic characteristics that real neural systems might display. Our results provide a solid system-theoretical foundation and simulation modeling framework for the high-performance computational support of such applications.
Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Aminmohammad Saberian
2014-01-01
Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.
Brown, Ramsay A; Swanson, Larry W
2013-09-01
Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.
Reversible neural stem cell niche dysfunction in a model of multiple sclerosis
DEFF Research Database (Denmark)
Rasmussen, Stine; Imitola, Jaime; Ayuso-Sacido, Angel
2011-01-01
OBJECTIVE: The subventricular zone (SVZ) of the brain constitutes a niche for neural stem and progenitor cells that can initiate repair after central nervous system (CNS) injury. In a relapsing-remitting model of experimental autoimmune encephalomyelitis (EAE), the neural stem cells (NSCs) become...
COMPUTER-SIMULATED NEURAL NETWORKS - AN APPROPRIATE MODEL FOR MOTOR DEVELOPMENT
VOS, JE; SCHEEPSTRA, KA
The idea of an artificial neural network is introduced in a historical context, and the essential aspect of it, viz., the modifiable synapse, is compared to the aspect of plasticity in the natural nervous system. Based on such an artificial neural network, a model is presented for the way in which
Spike-timing computation properties of a feed-forward neural network model
Directory of Open Access Journals (Sweden)
Drew Benjamin Sinha
2014-01-01
Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.
Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide
Directory of Open Access Journals (Sweden)
Armstrong F. Sompotan
2011-11-01
Full Text Available Detailed investigations of landslides are essential to understand fundamental landslide mechanisms. Seismic refraction method has been proven as a useful geophysical tool for investigating shallow landslides. The objective of this study is to introduce a new workflow using neural network in analyzing seismic refraction data and to compare the result with some methods; that are general reciprocal method (GRM and refraction tomography. The GRM is effective when the velocity structure is relatively simple and refractors are gently dipping. Refraction tomography is capable of modeling the complex velocity structures of landslides. Neural network is found to be more potential in application especially in time consuming and complicated numerical methods. Neural network seem to have the ability to establish a relationship between an input and output space for mapping seismic velocity. Therefore, we made a preliminary attempt to evaluate the applicability of neural network to determine velocity and elevation of subsurface synthetic models corresponding to arrival times. The training and testing process of the neural network is successfully accomplished using the synthetic data. Furthermore, we evaluated the neural network using observed data. The result of the evaluation indicates that the neural network can compute velocity and elevation corresponding to arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.
Directory of Open Access Journals (Sweden)
Poramate eManoonpong
2013-02-01
Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin
2013-01-01
Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.
Yang, Shiju; Li, Chuandong; Huang, Tingwen
2016-03-01
The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification
Elwalwal, Hatem Mostafa; Mahzan, Shahruddin Bin Hj.; Abdalla, Ahmed N.
2017-10-01
While pipes are cheaper than other means of transportation, this cost saving comes with a major price: pipes are subject to cracks, corrosion etc., which in turn can cause leakage and environmental damage. In this paper, Neural-Fuzzy model for multiple cracks classification based on Lamb Guide Wave. Simulation results for 42 sample were collected using ANSYS software. The current research object to carry on the numerical simulation and experimental study, aiming at finding an effective way to detection and the localization of cracks and holes defects in the main body of pipeline. Considering the damage form of multiple cracks and holes which may exist in pipeline, to determine the respective position in the steel pipe. In addition, the technique used in this research a guided lamb wave based structural health monitoring method whereas piezoelectric transducers will use as exciting and receiving sensors by Pitch-Catch method. Implementation of simple learning mechanism has been developed specially for the ANN for fuzzy the system represented.
SINs and SOMs: Neural microcircuits for size tuning in the zebrafish and mouse visual pathway.
Directory of Open Access Journals (Sweden)
Alison J. Barker
2013-05-01
Full Text Available In many animals, a fast and reliable circuit for discriminating between predator-sized objects and edible (prey-sized objects is necessary for survival. How are receptive fields in visual brain areas organized to extract information about size? Recent studies from the zebrafish optic tectum and the mouse visual cortex suggest de novo shaping of receptive fields by subtypes of inhibitory neurons. Del Bene et al. (2010 describe a population of GABAergic neurons in the zebrafish optic tectum (Superficial Interneurons, SINs that are necessary for size filtering during prey capture. Adesnik et al. (2012 describe a somatostatin-expressing interneuron population (SOMs that confers surround suppression on layer II/III pyramidal cells in mouse V1. Strikingly both the SINs and the SOMs, display size-dependent response properties. Increasing visual stimulus size increases excitatory input to these neurons. Dampening SIN or SOM activity alters tuning of neighboring circuits such that they lose preference for small objects. Both results provide exciting evidence for mechanisms of size filtering in visual circuits. Here we review the roles of the SINs and the SOMs and speculate on the similarity of such spatial filters across species.
Using artificial neural network approach for modelling rainfall–runoff ...
Indian Academy of Sciences (India)
driven techniques, the artificial neural .... inputs from the environment), one or more inter- mediate layers and an output layer (producing the ... three-layer learning network consisting of an input layer, a hidden layer and an output layer as illus-.
Neural model of gene regulatory network: a survey on supportive meta-heuristics.
Biswas, Surama; Acharyya, Sriyankar
2016-06-01
Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.
Maximum solid concentrations of coal water slurries predicted by neural network models
Energy Technology Data Exchange (ETDEWEB)
Cheng, Jun; Li, Yanchang; Zhou, Junhu; Liu, Jianzhong; Cen, Kefa
2010-12-15
The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation. (author)
Adaptive control using a hybrid-neural model: application to a polymerisation reactor
Directory of Open Access Journals (Sweden)
Cubillos F.
2001-01-01
Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.
QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
Predictions of Diffuse Pollution by the HSPF Model and the Back-Propagation Neural Network Model.
Chang, Chia-Ling; Li, Meng-Yuan
2017-08-01
Watershed models are important tools for predicting the possible change of watershed responses. Environmental models comprise the deterministic model and the probabilistic model. This study discusses the Hydrological Simulation Program Fortran (HSPF) and the Back-Propagation Neural Network (BPNN); these two models represent the deterministic model and the probabilistic model, respectively. As the properties of the two models are distinct, they have differing abilities to predict surface-runoff pollution. For the two models, the runoff simulation results are satisfactory. However, due to the limitation of the water quality monitoring records, pollution simulation is more difficult than runoff simulation. The results indicate that the prediction accuracy in the pollution simulation can be improved by adjusting the BPNN neurons. On the contrary, improving the prediction accuracy is limited by HSPF. Although the flexibility of BPNN is higher than HSPF, sufficient historical monitoring records are important for both of these models.
Antonietti, Alberto; Casellato, Claudia; Garrido, Jesús A; Luque, Niceto R; Naveros, Francisco; Ros, Eduardo; D' Angelo, Egidio; Pedrocchi, Alessandra
2016-01-01
In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes. This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical applications.
Neural Network Hydrological Modelling: Linear Output Activation Functions?
Abrahart, R. J.; Dawson, C. W.
2005-12-01
The power to represent non-linear hydrological processes is of paramount importance in neural network hydrological modelling operations. The accepted wisdom requires non-polynomial activation functions to be incorporated in the hidden units such that a single tier of hidden units can thereafter be used to provide a 'universal approximation' to whatever particular hydrological mechanism or function is of interest to the modeller. The user can select from a set of default activation functions, or in certain software packages, is able to define their own function - the most popular options being logistic, sigmoid and hyperbolic tangent. If a unit does not transform its inputs it is said to possess a 'linear activation function' and a combination of linear activation functions will produce a linear solution; whereas the use of non-linear activation functions will produce non-linear solutions in which the principle of superposition does not hold. For hidden units, speed of learning and network complexities are important issues. For the output units, it is desirable to select an activation function that is suited to the distribution of the target values: e.g. binary targets (logistic); categorical targets (softmax); continuous-valued targets with a bounded range (logistic / tanh); positive target values with no known upper bound (exponential; but beware of overflow); continuous-valued targets with no known bounds (linear). It is also standard practice in most hydrological applications to use the default software settings and to insert a set of identical non-linear activation functions in the hidden layer and output layer processing units. Mixed combinations have nevertheless been reported in several hydrological modelling papers and the full ramifications of such activities requires further investigation and assessment i.e. non-linear activation functions in the hidden units connected to linear or clipped-linear activation functions in the output unit. There are two
Neural networks for modeling gene-gene interactions in association studies
Directory of Open Access Journals (Sweden)
Bammann Karin
2009-12-01
Full Text Available Abstract Background Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons and five logistic regression models (the null model, three main effect models, and the full model with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied. Results The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction. Conclusions Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.
Development of Ensemble Neural Network Convection Parameterizations for Climate Models
Energy Technology Data Exchange (ETDEWEB)
Fox-Rabinovitz, M. S.; Krasnopolsky, V. M.
2012-05-02
The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an
Cognitive and Neural Modeling of Dynamics of Trust in Competitive Trustees
Hoogendoorn, M.; Jaffry, S.W.Q.; Treur, J.
2012-01-01
Trust dynamics can be modeled in relation to experiences. In this paper two models to represent human trust dynamics are introduced, namely a model on a cognitive level and a neural model. These models include a number of parameters, providing the possibility to express certain relations between
Olyaee, Saeed; Ebrahimpour, Reza; Hamedi, Samaneh; Jafarlou, Farzad M.
2009-08-01
Periodic nonlinearity is the main limitation on the accuracy of the nano-displacement measurements in the heterodyne interferometers. It is mainly produced by non-ideal polarized beams of the leaser and imperfect alignment of the optical components. In this paper, we model the periodic nonlinearity resulting from non-orthogonality and ellipticity of the laser beam by using combination of neural networks such as stacked generalization method and mixture of experts. The ensemble neural networks used for nonlinearity modeling are compared with single neural networks such as multi layer percepterons and radial basis function.
National Research Council Canada - National Science Library
M R Shukri; M M Rahman; D Ramasamy; K Kadirgama
2015-01-01
This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling...
National Research Council Canada - National Science Library
Jaensch, Stefan; Polifke, Wolfgang
2017-01-01
.... This article investigates the capability of artificial neural networks to serve as a basis for deducing nonlinear low-order models of the dynamics of a laminar flame from a Computational Fluid Dynamics (CFD) simulation...
Prediction of a model enzymatic acidolysis system using neural networks
Directory of Open Access Journals (Sweden)
Güven, Aytaç
2008-12-01
Full Text Available A model for the acidolysis of trinolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase was presented in this study. A neural networks (NN based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO 1,3-dipalmitoyl-2-oleoyl-glycerol (POP and triolein (OOO. Substrate ratio (SR, reaction temperature (T and reaction time (t were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, wass able to predict the reaction products concentration with a mean square error (MSE of less than 1.5 and R2 of 0.999. and explicit formulation of the proposed NN is presented. Considerable good performance is achieved in modeling the acidolysis reaction using neuronal networks.En este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO, 1,3-dipalmitoil-2-oleoil-glicerol (POP y trioleina (OOO. Se han usado como parámetros de entrada: la proporción del sustrato (SR, la temperatura de reacción (T y el tiempo de reacción (t. La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE de menos de 1.5 y una R2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de
Directory of Open Access Journals (Sweden)
Matthias Rybarsch
Full Text Available The brain keeps its overall dynamics in a corridor of intermediate activity and it has been a long standing question what possible mechanism could achieve this task. Mechanisms from the field of statistical physics have long been suggesting that this homeostasis of brain activity could occur even without a central regulator, via self-organization on the level of neurons and their interactions, alone. Such physical mechanisms from the class of self-organized criticality exhibit characteristic dynamical signatures, similar to seismic activity related to earthquakes. Measurements of cortex rest activity showed first signs of dynamical signatures potentially pointing to self-organized critical dynamics in the brain. Indeed, recent more accurate measurements allowed for a detailed comparison with scaling theory of non-equilibrium critical phenomena, proving the existence of criticality in cortex dynamics. We here compare this new evaluation of cortex activity data to the predictions of the earliest physics spin model of self-organized critical neural networks. We find that the model matches with the recent experimental data and its interpretation in terms of dynamical signatures for criticality in the brain. The combination of signatures for criticality, power law distributions of avalanche sizes and durations, as well as a specific scaling relationship between anomalous exponents, defines a universality class characteristic of the particular critical phenomenon observed in the neural experiments. Thus the model is a candidate for a minimal model of a self-organized critical adaptive network for the universality class of neural criticality. As a prototype model, it provides the background for models that may include more biological details, yet share the same universality class characteristic of the homeostasis of activity in the brain.
Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.
Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil
2016-10-01
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.
Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.
Directory of Open Access Journals (Sweden)
Yoonsik Shim
2016-10-01
Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.
Modeling of the height control system using artificial neural networks
Directory of Open Access Journals (Sweden)
A. R Tahavvor
2016-09-01
Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of
Application of Neural Network and Simulation Modeling to Evaluate Russian Banks’ Performance
Sharma, Satish; Shebalkov, Mikhail
2013-01-01
This paper presents an application of neural network and simulation modeling to analyze and predict the performance of 883 Russian Banks over the period 2000-2010. Correlation analysis was performed to obtain key financial indicators which reflect the leverage, liquidity, profitability and size of Banks. Neural network was trained over the entire dataset, and then simulation modeling was performed generating values which are distributed with Largest Extreme Value and Loglogistic distributions...
PREDICTION OF FINANCIAL FAILURE OF BANKS BY ARTIFICAL NEURAL NETWORK MODEL
Directory of Open Access Journals (Sweden)
Utku ALTUNÖZ
2013-12-01
Full Text Available In this article financial failure prediction models based on artificial neural networkmodel, which is among the multivariable statistical techniques has been tested in a parallelwith literature surveys. As a result of the study, it has been observed that the power ofartificial neural network model in terms of predicting financial failure give a highprobability for both 1 and 2 years before the financial failure.
Neural Correlates of the Y Chromosome in Autism: XYY Syndrome as a Genetic Model
2017-09-01
AWARD NUMBER: W81XWH-15-1-0355 TITLE: Neural Correlates of the Y Chromosome in Autism: XYY Syndrome as a Genetic Model PRINCIPAL INVESTIGATOR...by ANSI Std. Z39.18 Neural Correlates of the Y Chromosome in Autism: XYY Syndrome as a Genetic Model Table of Contents Page 1. Introduction...heterogeneous with regard to etiological/risk factors, pathogenesis, and clinical presentations. Heritability studies have shown that genetic
A New Animal Model for Developing a Somatosensory Neural Interface for Prosthetic Limbs
2008-02-12
interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D. University of Pittsburgh 1 10/15/2007 Scientific progress and accomplishments. We...information to the brain. A new animal model for developing a somatosensory neural interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D...A new animal model for developing a somatosensory neural interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D. University of Pittsburgh
A deep convolutional neural network model to classify heartbeats.
Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San
2017-10-01
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.
Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Vinay Chandwani
2014-01-01
Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.
Bianchi, Daniela; De Michele, Pasquale; Marchetti, Cristina; Tirozzi, Brunello; Cuomo, Salvatore; Marie, Hélène; Migliore, Michele
2014-02-01
The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB-based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD. © 2013 Wiley Periodicals, Inc.
Reliability Modeling of Microelectromechanical Systems Using Neural Networks
Perera. J. Sebastian
2000-01-01
Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.
Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions
Directory of Open Access Journals (Sweden)
X. Mu
2014-09-01
Full Text Available The memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model can approximate the flux-charge relation of existing piecewise linear memristor model, window function memristor model, and a physical memristor device.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate......In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
2009-01-01
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
Modeling the behavioral substrates of associate learning and memory - Adaptive neural models
Lee, Chuen-Chien
1991-01-01
Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.
Neural Network Model for Prediction of Discharged from the Catchments of Langat River, Malaysia
Directory of Open Access Journals (Sweden)
Z. Ahmad
2010-09-01
Full Text Available Artificial neural networks have been shown to be able to approximate any continuous non-linear functions and have been used to build data base empirical models for non-linear processes. In this study, neural networks models were used to model the daily river flows or discharged in Langat River, Malaysia. Two possible ways of modelling were implemented which is by time series prediction and by the dynamics function of the system which include the past value of the discharged and also the rainfall in the input. The sum square error (SSE, residue analysis and correlation coefficient based on the observed and prediction output is chosen as the criteria of selection of which models is appropriate. It was found that the developed neural networks models using dynamics function provided satisfactory model discharges.
Obtaining raised density connections by thermosonic microwelding in 3D integrated microcircuits
Directory of Open Access Journals (Sweden)
Lanin V. L.
2014-06-01
Full Text Available The authors consider the processes of obtaining raised density microwelded connections in 3D-integrated microcircuits by the thermosonic microwelding. The processes include the use of the raised frequencies of ultrasound, application of the microinstrument with a thinning of the working end and precision devices for ball formation, which provide reproducibility of connections quality. At a small step of contact pads, the use of a wire of small diameter (not more than 25 µm is necessary for devices with a multilevel arrangement of leads and chess arrangement of contact pads on the chip, providing the maximum length of the formed crosspieces does not exceed 4—5 mm.
Teverovsky, Alexander; Sahu, Kusum
2003-01-01
Potential users of plastic encapsulated microcircuits (PEMs) need to be reminded that unlike the military system of producing robust high-reliability microcircuits that are designed to perform acceptably in a variety of harsh environments, PEMs are primarily designed for use in benign environments where equipment is easily accessed for repair or replacement. The methods of analysis applied to military products to demonstrate high reliability cannot always be applied to PEMs. This makes it difficult for users to characterize PEMs for two reasons: 1. Due to the major differences in design and construction, the standard test practices used to ensure that military devices are robust and have high reliability often cannot be applied to PEMs that have a smaller operating temperature range and are typically more frail and susceptible to moisture absorption. In contrast, high-reliability military microcircuits usually utilize large, robust, high-temperature packages that are hermetically sealed. 2. Unlike the military high-reliability system, users of PEMs have little visibility into commercial manufacturers proprietary design, materials, die traceability, and production processes and procedures. There is no central authority that monitors PEM commercial product for quality, and there are no controls in place that can be imposed across all commercial manufacturers to provide confidence to high-reliability users that a common acceptable level of quality exists for all PEMs manufacturers. Consequently, there is no guaranteed control over the type of reliability that is built into commercial product, and there is no guarantee that different lots from the same manufacturer are equally acceptable. And regarding application, there is no guarantee that commercial products intended for use in benign environments will provide acceptable performance and reliability in harsh space environments. The qualification and screening processes contained in this document are intended to
On the nature, modeling, and neural basis of social ties
van Winden, F.; Stallen, M.; Ridderinkhof, K.R.; Houser, D.; McCabe, K.
2009-01-01
Purpose This paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility interdependence’).
Bayesian model ensembling using meta-trained recurrent neural networks
Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.
2017-01-01
In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian
On the Nature, Modeling, and Neural Bases of Social Ties
F.A.A.M. Winden, van (Frans); M. Stallen (Mirre); K.R. Ridderinkhof (Richard)
2008-01-01
textabstractThis paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility
A Bayesian framework for simultaneously modeling neural and behavioral data
Turner, B.M.; Forstmann, B.U.; Wagenmakers, E.-J.; Brown, S.D.; Sederberg, P.B.; Steyvers, M.
2013-01-01
Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by
Using artificial neural network approach for modelling rainfall–runoff ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 122; Issue 2. Using artificial neural network approach for ... In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood ...
Rodríguez, J; Clemente, G; Sanjuán, N; Bon, J
2014-01-01
The drying kinetics of thyme was analyzed by considering different conditions: air temperature of between 40°C and 70°C , and air velocity of 1 m/s. A theoretical diffusion model and eight different empirical models were fitted to the experimental data. From the theoretical model application, the effective diffusivity per unit area of the thyme was estimated (between 3.68 × 10(-5) and 2.12 × 10 (-4) s(-1)). The temperature dependence of the effective diffusivity was described by the Arrhenius relationship with activation energy of 49.42 kJ/mol. Eight different empirical models were fitted to the experimental data. Additionally, the dependence of the parameters of each model on the drying temperature was determined, obtaining equations that allow estimating the evolution of the moisture content at any temperature in the established range. Furthermore, artificial neural networks were developed and compared with the theoretical and empirical models using the percentage of the relative errors and the explained variance. The artificial neural networks were found to be more accurate predictors of moisture evolution with VAR ≥ 99.3% and ER ≤ 8.7%.
Assimilation of neural network soil moisture in land surface models
Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias
2017-04-01
In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests
Directory of Open Access Journals (Sweden)
Dawei Han
2012-02-01
Full Text Available The application of ANNs (Artifi cial Neural Networks has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.
Uniform neural tissue models produced on synthetic hydrogels using standard culture techniques.
Barry, Christopher; Schmitz, Matthew T; Propson, Nicholas E; Hou, Zhonggang; Zhang, Jue; Nguyen, Bao K; Bolin, Jennifer M; Jiang, Peng; McIntosh, Brian E; Probasco, Mitchell D; Swanson, Scott; Stewart, Ron; Thomson, James A; Schwartz, Michael P; Murphy, William L
2017-11-01
The aim of the present study was to test sample reproducibility for model neural tissues formed on synthetic hydrogels. Human embryonic stem (ES) cell-derived precursor cells were cultured on synthetic poly(ethylene glycol) (PEG) hydrogels to promote differentiation and self-organization into model neural tissue constructs. Neural progenitor, vascular, and microglial precursor cells were combined on PEG hydrogels to mimic developmental timing, which produced multicomponent neural constructs with 3D neuronal and glial organization, organized vascular networks, and microglia with ramified morphologies. Spearman's rank correlation analysis of global gene expression profiles and a comparison of coefficient of variation for expressed genes demonstrated that replicate neural constructs were highly uniform to at least day 21 for samples from independent experiments. We also demonstrate that model neural tissues formed on PEG hydrogels using a simplified neural differentiation protocol correlated more strongly to in vivo brain development than samples cultured on tissue culture polystyrene surfaces alone. These results provide a proof-of-concept demonstration that 3D cellular models that mimic aspects of human brain development can be produced from human pluripotent stem cells with high sample uniformity between experiments by using standard culture techniques, cryopreserved cell stocks, and a synthetic extracellular matrix. Impact statement Pluripotent stem (PS) cells have been characterized by an inherent ability to self-organize into 3D "organoids" resembling stomach, intestine, liver, kidney, and brain tissues, offering a potentially powerful tool for modeling human development and disease. However, organoid formation must be quantitatively reproducible for applications such as drug and toxicity screening. Here, we report a strategy to produce uniform neural tissue constructs with reproducible global gene expression profiles for replicate samples from multiple
Directory of Open Access Journals (Sweden)
Jesper Mogensen
2017-04-01
Full Text Available In the present paper it is argued that the “neural correlate of consciousness” (NCC does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions and REFCON (of elementary functions and consciousness models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions.
Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.
Tiğdemir, Mesut
2014-01-01
Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.
Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network
Yao, Weigang; Liou, Meng-Sing
2012-01-01
The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis
Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.
Directory of Open Access Journals (Sweden)
Mesut Tiğdemir
Full Text Available Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO. More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.
Morphogens, modeling and patterning the neural tube: an interview with James Briscoe.
Briscoe, James
2015-01-20
James Briscoe has a BSc in Microbiology and Virology (from the University of Warwick, UK) and a PhD in Molecular and Cellular Biology (from the Imperial Cancer Research Fund, London, now Cancer Research UK). He started working on the development of the neural tube in the lab of Tom Jessel as a postdoctoral fellow, establishing that there was graded sonic hedgehog signaling in the ventral neural tube. He is currently a group leader and Head of Division in Developmental Biology at the MRC National Institute for Medical Research (which will become part of the Francis Crick Institute in April 2015). He is working to understand the molecular and cellular mechanisms of graded signaling in the vertebrate neural tube.We interviewed him about the development of ideas on morphogenetic gradients and his own work on modeling the development of the neural tube for our series on modeling in biology.
Directory of Open Access Journals (Sweden)
R. H. R. Garcel1
2015-09-01
Full Text Available AbstractIn the present study a preliminary neural network modelling to improve our understanding of Recombinant Human Erythropoietin purification process in a plant was explored. A three layer feed-forward back propagation neural network was constructed for predicting the efficiency of the purification section comprising four chromatographic steps as a function of eleven operational variables. The neural network model performed very well in the training and validation phases. Using the connection weight method the predictor variables were ranked based on their estimated explanatory importance in the neural network and five input variables were found to be predominant over the others. These results provided useful information showing that the first chromatographic step and the third chromatographic step are decisive to achieve high efficiencies in the purification section, thus enriching the control strategy of the plant.
Fernández, Michael; Caballero, Julio
2006-10-01
Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.
DEFF Research Database (Denmark)
Chon, K H; Holstein-Rathlou, N H; Marsh, D J
1998-01-01
kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained......In this paper, feedforward neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading...... via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general....
Visually-salient contour detection using a V1 neural model with horizontal connections
Loxley, P N
2011-01-01
A convolution model which accounts for neural activity dynamics in the primary visual cortex is derived and used to detect visually salient contours in images. Image inputs to the model are modulated by long-range horizontal connections, allowing contextual effects in the image to determine visual saliency, i.e. line segments arranged in a closed contour elicit a larger neural response than line segments forming background clutter. The model is tested on 3 types of contour, including a line, a circular closed contour, and a non-circular closed contour. Using a modified association field to describe horizontal connections the model is found to perform well for different parameter values. For each type of contour a different facilitation mechanism is found. Operating as a feed-forward network, the model assigns saliency by increasing the neural activity of line segments facilitated by the horizontal connections. Alternatively, operating as a feedback network, the model can achieve further improvement over sever...
Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung
2018-02-01
Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung
2018-02-01
Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
An avian model for the reversal of neurobehavioral teratogenicity with neural stem cells
Dotan, Sharon; Pinkas, Adi; Slotkin, Theodore A.; Yanai, Joseph
2010-01-01
A fast and simple model which uses lower animals on the evolutionary scale is beneficial for developing procedures for the reversal of neurobehavioral teratogenicity with neural stem cells. Here, we established a procedure for the derivation of chick neural stem cells, establishing embryonic day (E) 10 as optimal for progression to neuronal phenotypes. Cells were obtained from the embryonic cerebral hemispheres and incubated for 5–7 days in enriched medium containing epidermal growth factor (...
Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.
Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve
2011-11-01
Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.
Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
Artificial neural network models for biomass gasification in fluidized bed gasifiers
DEFF Research Database (Denmark)
Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles
2013-01-01
Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...
A Neural Network Model of the Structure and Dynamics of Human Personality
Read, Stephen J.; Monroe, Brian M.; Brownstein, Aaron L.; Yang, Yu; Chopra, Gurveen; Miller, Lynn C.
2010-01-01
We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two…
Fault diagnosis for engine air path with neural models and classifier ...
African Journals Online (AJOL)
The method uses an independent radial basis function (RBF) neural network model to model engine dynamics, and the modelling errors are used to form the basis for ... The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range.
Control of nonlinear chemical processes using neural models and feedback linearization
te Braake, Hubert A.B.; van Can, Eric J.L.; Scherpen, Jacquelien M.A.; Verbruggen, Henk B.
1998-01-01
Black-box modeling techniques based on artificial neural networks are opening new horizons for the modeling and control nonlinear processes in biotechnology and the chemical process industries. The link between dynamic process models and actual process control is provided by the concept of
Neural Population Dynamics Modeled by Mean-Field Graphs
Kozma, Robert; Puljic, Marko
2011-09-01
In this work we apply random graph theory approach to describe neural population dynamics. There are important advantages of using random graph theory approach in addition to ordinary and partial differential equations. The mathematical theory of large-scale random graphs provides an efficient tool to describe transitions between high- and low-dimensional spaces. Recent advances in studying neural correlates of higher cognition indicate the significance of sudden changes in space-time neurodynamics, which can be efficiently described as phase transitions in the neuropil medium. Phase transitions are rigorously defined mathematically on random graph sequences and they can be naturally generalized to a class of percolation processes called neuropercolation. In this work we employ mean-field graphs with given vertex degree distribution and edge strength distribution. We demonstrate the emergence of collective oscillations in the style of brains.
Directory of Open Access Journals (Sweden)
Lijun Zhang
2018-02-01
Full Text Available Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.
Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems
Fusaro, Robert L.
1998-01-01
Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.
Directory of Open Access Journals (Sweden)
Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
[GSH fermentation process modeling using entropy-criterion based RBF neural network model].
Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng
2008-05-01
The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.
Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan
Li, Y.; Tang, C.; Xu, L.; Ye, S.
2017-07-01
Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan’s water situation will become better.
Bias-dependent hybrid PKI empirical-neural model of microwave FETs
Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera
2011-10-01
Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.
[Robustness analysis of adaptive neural network model based on spike timing-dependent plasticity].
Chen, Yunzhi; Xu, Guizhi; Zhou, Qian; Guo, Miaomiao; Guo, Lei; Wan, Xiaowei
2015-02-01
To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.
Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair
Directory of Open Access Journals (Sweden)
Sara L Gonzalez Andino
2011-07-01
Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.
Application of neural models as controllers in mobile robot velocity control loop
Cerkala, Jakub; Jadlovska, Anna
2017-01-01
This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.
Directory of Open Access Journals (Sweden)
Guanbin Gao
2017-01-01
Full Text Available Articulated arm coordinate measuring machine (AACMM is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM.
Standard representation and unified stability analysis for dynamic artificial neural network models.
Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D
2017-12-02
An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.
A study on the forecasting of daily stream flow using the multilayer neural networks model
Energy Technology Data Exchange (ETDEWEB)
Kim, Sung-Won [Colorado State University, Fort Collins, CO(United States)
2000-10-31
In this study, Neural Networks models were used to forecast daily stream flow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily stream flow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily stream flow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Model(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more systematic approach, reduce model parameters, and shorten the time spent in the model development. (author). 22 refs., 9 tabs., 7 figs.
Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
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Ireneusz Zagórski
2017-09-01
Full Text Available The paper reports the results of artificial neural network modelling of vibration in. a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called “black box” model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function and MLP (Multi-Layered Perceptron.
Robust Quasi-LPV Control Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2002-01-01
In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of LMIs with added constraints, some implementation issues are addressed and a simulation example is presented....
An Efficient Neural Network Based Modeling Method for Automotive EMC Simulation
Frank, Florian; Weigel, Robert
2011-09-01
This paper presents a newly developed methodology for VHDL-AMS model integration into SPICE-based EMC simulations. To this end the VHDL-AMS model, which is available in a compiled version only, is characterized under typical loading conditions, and afterwards a neural network based technique is applied to convert characteristic voltage and current data into an equivalent circuit in SPICE syntax. After the explanation of the whole method and the presentation of a newly developed switched state space dynamic neural network model, the entire analysis process is demonstrated using a typical application from automotive industry.
Hybrid neural modelling of an anaerobic digester with respect to biological constraints.
Karama, A; Bernard, O; Gouzé, J L; Benhammou, A; Dochain, D
2001-01-01
A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.
Towards a mathematical theory of cortical micro-circuits.
Directory of Open Access Journals (Sweden)
Dileep George
2009-10-01
Full Text Available The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM, can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.
Hu, Scott B; Wong, Deborah J L; Correa, Aditi; Li, Ning; Deng, Jane C
2016-01-01
Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates. Retrospective cohort study. The hematologic malignancy unit in an academic medical center in the United States. Adult patients admitted to the hematologic malignancy unit from 2009 to 2010. None. Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively. We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.
Hybrid model decomposition of speech and noise in a radial basis function neural model framework
DEFF Research Database (Denmark)
Sørensen, Helge Bjarup Dissing; Hartmann, Uwe
1994-01-01
The aim of the paper is to focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the presence of additive car noise. The technique...... applied is based on a combination of the hidden Markov model (HMM) decomposition method, for speech recognition in noise, developed by Varga and Moore (1990) from DRA and the hybrid (HMM/RBF) recognizer containing hidden Markov models and radial basis function (RBF) neural networks, developed by Singer...... and Lippmann (1992) from MIT Lincoln Lab. The present authors modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. The approach has been denoted the hybrid model decomposition method and it provides an optimal method...
Using chaotic artificial neural networks to model memory in the brain
Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh
2017-03-01
In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.
Neural network emulation of the integral equation model with multiple scattering.
Pulvirenti, Luca; Ticconi, Francesca; Pierdicca, Nazzareno
2009-01-01
The Integral Equation Model with multiple scattering (IEMM) represents a well-established method that provides a theoretical framework for the scattering of electromagnetic waves from rough surfaces. A critical aspect is the long computational time required to run such a complex model. To deal with this problem, a neural network technique is proposed in this work. In particular, we have adopted neural networks to reproduce the backscattering coefficients predicted by IEMM at L- and C-bands, thus making reference to presently operative satellite radar sensors, i.e., that aboard ERS-2, ASAR on board ENVISAT (C-band), and PALSAR aboard ALOS (L-band). The neural network-based model has been designed for radar observations of both flat and tilted surfaces, in order to make it applicable for hilly terrains too. The assessment of the proposed approach has been carried out by comparing neural network-derived backscattering coefficients with IEMM-derived ones. Different databases with respect to those employed to train the networks have been used for this purpose. The outcomes seem to prove the feasibility of relying on a neural network approach to efficiently and reliably approximate an electromagnetic model of surface scattering.
Luo, Junhui; Wu, Chao; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun
2018-01-01
At present, the prediction of soft foundation settlement mostly use the exponential curve and hyperbola deferred approximation method, and the correlation between the results is poor. However, the application of neural network in this area has some limitations, and none of the models used in the existing cases adopted the TS fuzzy neural network of which calculation combines the characteristics of fuzzy system and neural network to realize the mutual compatibility methods. At the same time, the developed and optimized calculation program is convenient for engineering designers. Taking the prediction and analysis of soft foundation settlement of gully soft soil in granite area of Guangxi Guihe road as an example, the fuzzy neural network model is established and verified to explore the applicability. The TS fuzzy neural network is used to construct the prediction model of settlement and deformation, and the corresponding time response function is established to calculate and analyze the settlement of soft foundation. The results show that the prediction of short-term settlement of the model is accurate and the final settlement prediction result has certain engineering reference value.
Directory of Open Access Journals (Sweden)
Natalia A. Tomashenko
2016-11-01
Full Text Available Subject of Research. We study speaker adaptation of deep neural network (DNN acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particular speaker. Method. A novel method for training and adaptation of deep neural network acoustic models has been developed. It is based on using an auxiliary GMM (Gaussian Mixture Models model and GMMD (GMM-derived features. The principle advantage of the proposed GMMD features is the possibility of performing the adaptation of a DNN through the adaptation of the auxiliary GMM. In the proposed approach any methods for the adaptation of the auxiliary GMM can be used, hence, it provides a universal method for transferring adaptation algorithms developed for GMMs to DNN adaptation.Main Results. The effectiveness of the proposed approach was shown by means of one of the most common adaptation algorithms for GMM models – MAP (Maximum A Posteriori adaptation. Different ways of integration of the proposed approach into state-of-the-art DNN architecture have been proposed and explored. Analysis of choosing the type of the auxiliary GMM model is given. Experimental results on the TED-LIUM corpus demonstrate that, in an unsupervised adaptation mode, the proposed adaptation technique can provide, approximately, a 11–18% relative word error reduction (WER on different adaptation sets, compared to the speaker-independent DNN system built on conventional features, and a 3–6% relative WER reduction compared to the SAT-DNN trained on fMLLR adapted features.
Optimization of artificial neural networks used for retention modelling in ion chromatography.
Srecnik, Goran; Debeljak, Zeljko; Cerjan-Stefanović, Stefica; Novic, Milko; Bolancab, Tomislav
2002-10-11
The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion
Coombes, S.; Venkov, N.A.; Shiau, L.; Bojak, I.; Liley, D.T.; Laing, C.R.
2007-01-01
Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal
Neural networks simulation of a discrete model of continious effects of irrelevant stimuli
Molenaar, P.C.M.
1990-01-01
Presents a general simulation method based on minimal neural network representations of nonmathematical, structural models of information processes. The time-dependent behavior of each component in a given structural model is represented by a simple, noncommittal equation that does not affect the
Neural Network Based Model of an Industrial Oil-Fired Boiler System ...
African Journals Online (AJOL)
In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.
A Neural Network Model of the Visual Short-Term Memory
DEFF Research Database (Denmark)
Petersen, Anders; Kyllingsbæk, Søren; Hansen, Lars Kai
2009-01-01
In this paper a neural network model of Visual Short-Term Memory (VSTM) is presented. The model links closely with Bundesen’s (1990) well-established mathematical theory of visual attention. We evaluate the model’s ability to fit experimental data from a classical whole and partial report study...
Folk music style modelling by recurrent neural networks with long short term memory units
Sturm, Bob,; Santos, João Felipe; Korshunova, Iryna
2015-01-01
We demonstrate two generative models created by training a recurrent neural network (RNN) with three hidden layers of long short-term memory (LSTM) units. This extends past work in numerous directions, including training deeper models with nearly 24,000 high-level transcriptions of folk tunes. We discuss our on-going work.
Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networks.
Winkler, David A; Burden, Frank R
2007-01-01
Methods for predicting the binding affinity of peptides to the MHC have become more sophisticated in the past 5-10 years. It is possible to use computational quantitative structure-activity methods to build models of peptide affinity that are truly predictive. Two of the most useful methods for building models are Bayesian regularized neural networks for continuous or discrete (categorical) data and support vector machines (SVMs) for discrete data. We illustrate the application of Bayesian regularized neural networks to modeling MHC class II-binding affinity of peptides. Training data comprised sequences and binding data for nonamer (nine amino acid) peptides. Peptides were characterized by mathematical representations of several types. Independent test data comprised sequences and binding data for peptides of length Bayesian neural networks are robust, efficient "universal approximators" that are well able to tackle the difficult problem of correctly predicting the MHC class II-binding activities of a majority of the test set peptides.
Model-Following Controller Based on Neural Network for Variable Displacement Pump
Chu, Ming-Hui; Kang, Yuan; Chang, Yih-Fong; Liu, Yuan-Liang; Chang, Chuan-Wei
The variable displacement axial piston pump (VDAPP) is inherently nonlinear, time variant and subjected to load disturbance. The controls of flow and pressure of VDAPP are achieved by changing the swashplate angle. The swashplate actuators are controlled by an electro-hydraulic proportional valve (EHPV). It is reasonable for swashplate angle of a VDAPP to employ neural network based on adaptive control. In this study, the nonlinear model of the VDAPP with a three-way electro-hydraulic proportional valve is proposed, and a neural network model-following controller is designed to control the swashplate swivel angle. The time response for the swashplate angle is analyzed by simulation and experiment, and a favorable model-following characteristic is achieved. The proposed neural controller can conduct nonlinear control in VDAPP, enhance adaptability and robustness, and improve the performance of the control system.
Lebedev, Dmitry V; Steil, Jochen J; Ritter, Helge J
2005-04-01
We introduce a new type of neural network--the dynamic wave expansion neural network (DWENN)--for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.
Nanoelectronics enabled chronic multimodal neural platform in a mouse ischemic model.
Luan, Lan; Sullender, Colin T; Li, Xue; Zhao, Zhengtuo; Zhu, Hanlin; Wei, Xiaoling; Xie, Chong; Dunn, Andrew K
2017-12-04
Despite significant advancements of optical imaging techniques for mapping hemodynamics in small animal models, it remains challenging to combine imaging with spatially resolved electrical recording of individual neurons especially for longitudinal studies. This is largely due to the strong invasiveness to the living brain from the penetrating electrodes and their limited compatibility with longitudinal imaging. We implant arrays of ultraflexible nanoelectronic threads (NETs) in mice for neural recording both at the brain surface and intracortically, which maintain great tissue compatibility chronically. By mounting a cranial window atop of the NET arrays that allows for chronic optical access, we establish a multimodal platform that combines spatially resolved electrical recording of neural activity and laser speckle contrast imaging (LSCI) of cerebral blood flow (CBF) for longitudinal studies. We induce peri-infarct depolarizations (PIDs) by targeted photothrombosis, and show the ability to detect its occurrence and propagation through spatiotemporal variations in both extracellular potentials and CBF. We also demonstrate chronic tracking of single-unit neural activity and CBF over days after photothrombosis, from which we observe reperfusion and increased firing rates. This multimodal platform enables simultaneous mapping of neural activity and hemodynamic parameters at the microscale for quantitative, longitudinal comparisons with minimal perturbation to the baseline neurophysiology. The ability to spatiotemporally resolve and chronically track CBF and neural electrical activity in the same living brain region has broad applications for studying the interplay between neural and hemodynamic responses in health and in cerebrovascular and neurological pathologies. Copyright © 2017 Elsevier B.V. All rights reserved.
Hoppensteadt, F C; Izhikevich, E M
1996-08-01
We study weakly connected networks of neural oscillators near multiple Andronov-Hopf bifurcation points. We analyze relationships between synaptic organizations (anatomy) of the networks and their dynamical properties (function). Our principal assumptions are: (1) Each neural oscillator comprises two populations of neurons; excitatory and inhibitory ones; (2) activity of each population of neurons is described by a scalar (one-dimensional) variable; (3) each neural oscillator is near a nondegenerate supercritical Andronov-Hopf bifurcation point; (4) the synaptic connections between the neural oscillators are weak. All neural networks satisfying these hypotheses are governed by the same dynamical system, which we call the canonical model. Studying the canonical model shows that: (1) A neural oscillator can communicate only with those oscillators which have roughly the same natural frequency. That is, synaptic connections between a pair of oscillators having different natural frequencies are functionally insignificant. (2) Two neural oscillators having the same natural frequencies might not communicate if the connections between them are from among a class of pathological synaptic configurations. In both cases the anatomical presence of synaptic connections between neural oscillators does not necessarily guarantee that the connections are functionally significant. (3) There can be substantial phase differences (time delays) between the neural oscillators, which result from the synaptic organization of the network, not from the transmission delays. Using the canonical model we can illustrate self-ignition and autonomous quiescence (oscillator death) phenomena. That is, a network of passive elements can exhibit active properties and vice versa. We also study how Dale's principle affects dynamics of the networks, in particular, the phase differences that the network can reproduce. We present a complete classification of all possible synaptic organizations from this
Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong
2013-11-01
In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Heekyung eLee
2014-02-01
Full Text Available Cognitive symptoms are core features of mental disorders but procognitive treatments are limited. We have proposed a ‘discoordination’ hypothesis that cognitive impairment results from aberrant coordination of neural activity. We reported that neonatal ventral hippocampus lesion (NVHL rats, an established neurodevelopmental model of schizophrenia, have abnormal neural synchrony and cognitive deficits in the active place avoidance task. During stillness, we observed that cortical local field potentials sometimes resembled epileptiform spike-wave discharges with higher prevalence in NVHL rats, indicating abnormal neural synchrony due perhaps to imbalanced excitation-inhibition coupling. Here, within the context of the hypothesis, we investigated whether attenuating abnormal neural synchrony will improve cognition in NVHL rats. We report that 1 interhippocampal synchrony in the theta and beta bands is correlated with active place avoidance performance; 2 the anticonvulsant ethosuximide attenuated the abnormal spike-wave activity, improved cognitive control, and reduced hyperlocomotion; 3 ethosuximide normalized the task-associated theta and beta synchrony between the two hippocampi but also increased synchrony between the medial prefrontal cortex and hippocampus above control levels; 4 the antipsychotic olanzapine was less effective at improving cognitive control and normalizing place avoidance-related inter-hippocampal neural synchrony, although it reduced hyperactivity; and 5 olanzapine caused an abnormal pattern of frequency-independent increases in neural synchrony, in both NVHL and control rats. These data suggest that normalizing aberrant neural synchrony can be beneficial and that drugs targeting the pathophysiology of abnormally coordinated neural activities may be a promising theoretical framework and strategy for developing treatments that improve cognition in neurodevelopmental disorders such as schizophrenia.
Directory of Open Access Journals (Sweden)
Murat Cuhadar
2014-03-01
Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.
Modeling anterior development in mice: diet as modulator of risk for neural tube defects.
Kappen, Claudia
2013-11-01
Head morphogenesis is a complex process that is controlled by multiple signaling centers. The most common defects of cranial development are craniofacial defects, such as cleft lip and cleft palate, and neural tube defects, such as anencephaly and encephalocoele in humans. More than 400 genes that contribute to proper neural tube closure have been identified in experimental animals, but only very few causative gene mutations have been identified in humans, supporting the notion that environmental influences are critical. The intrauterine environment is influenced by maternal nutrition, and hence, maternal diet can modulate the risk for cranial and neural tube defects. This article reviews recent progress toward a better understanding of nutrients during pregnancy, with particular focus on mouse models for defective neural tube closure. At least four major patterns of nutrient responses are apparent, suggesting that multiple pathways are involved in the response, and likely in the underlying pathogenesis of the defects. Folic acid has been the most widely studied nutrient, and the diverse responses of the mouse models to folic acid supplementation indicate that folic acid is not universally beneficial, but that the effect is dependent on genetic configuration. If this is the case for other nutrients as well, efforts to prevent neural tube defects with nutritional supplementation may need to become more specifically targeted than previously appreciated. Mouse models are indispensable for a better understanding of nutrient-gene interactions in normal pregnancies, as well as in those affected by metabolic diseases, such as diabetes and obesity. © 2013 Wiley Periodicals, Inc.
Neural computations underlying arbitration between model-based and model-free learning
Lee, Sang Wan; Shimojo, Shinsuke; O’Doherty, John P.
2014-01-01
SUMMARY There is accumulating neural evidence to support the existence of two distinct systems for guiding action-selection in the brain, a deliberative “model-based” and a reflexive “model-free” system. However, little is known about how the brain determines which of these systems controls behavior at one moment in time. We provide evidence for an arbitration mechanism that allocates the degree of control over behavior by model-based and model-free systems as a function of the reliability of their respective predictions. We show that inferior lateral prefrontal and frontopolar cortex encode both reliability signals and the output of a comparison between those signals, implicating these regions in the arbitration process. Moreover, connectivity between these regions and model-free valuation areas is negatively modulated by the degree of model-based control in the arbitrator, suggesting that arbitration may work through modulation of the model-free valuation system when the arbitrator deems that the model-based system should drive behavior. PMID:24507199
Similarity on neural stem cells and brain tumor stem cells in transgenic brain tumor mouse models
Qiao, Guanqun; Li, Qingquan; Peng, Gang; Ma, Jun; Fan, Hongwei; Li, Yingbin
2013-01-01
Although it is believed that glioma is derived from brain tumor stem cells, the source and molecular signal pathways of these cells are still unclear. In this study, we used stable doxycycline-inducible transgenic mouse brain tumor models (c-myc+/SV40Tag+/Tet-on+) to explore the malignant trans-formation potential of neural stem cells by observing the differences of neural stem cells and brain tumor stem cells in the tumor models. Results showed that chromosome instability occurred in brain t...
An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking
DEFF Research Database (Denmark)
Shaikh, Danish; Manoonpong, Poramate
2016-01-01
Acoustic tracking of a moving sound source is relevant in many domains including robotic phonotaxis and human-robot interaction. Typical approaches rely on processing time-difference-of-arrival cues obtained via multi-microphone arrays with Kalman or particle filters, or other computationally...... expensive algorithms. We present a novel bioinspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural...
Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Stoustrup, Jakob
2003-01-01
This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....
Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B
2016-08-01
Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous
You, Mengxian; Mou, Zongxia
2017-07-01
This paper implemented a model study of combined electrical and near-infrared (808 nm) neural stimulation (NINS) on the bullfrog sciatic nerve. The model includes a COMSOL model to calculate the electric-field distribution of the surrounding area of the nerve, a Monte Carlo model to simulate light transport and absorption in the bullfrog sciatic nerve during NINS, and a NEURON model to simulate the neural electrophysiology changes under electrical stimulus and laser irradiation. The optical thermal effect is considered the main mechanism during NINS. Therefore, thermal change during laser irradiation was calculated by the Monte Carlo method, and the temperature distribution was then transferred to the NEURON model to stimulate the sciatic nerve. The effects on thermal response by adjusting the laser spot size, energy of the beam, and the absorption coefficient of the nerve are analyzed. The effect of the ambient temperature on the electrical stimulation or laser stimulation and the interaction between laser irradiation and electrical stimulation are also studied. The results indicate that the needed stimulus threshold for neural activation or inhibition is reduced by laser irradiation. Additionally, the needed laser energy for blocking the action potential is reduced by electrical stimulus. Both electrical and laser stimulation are affected by the ambient temperature. These results provide references for subsequent animal experiments and could be of great help to future basic and applied studies of infrared neural stimulation (INS).
Synchronized stability in a reaction–diffusion neural network model
Energy Technology Data Exchange (ETDEWEB)
Wang, Ling; Zhao, Hongyong, E-mail: hongyongz@126.com
2014-11-14
The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.
Muñetón-Gómez, Vilma C.; Doncel-Pérez, Ernesto; Fernandez, Ana P.; Serrano, Julia; Pozo-Rodrigálvarez, Andrea; Vellosillo-Huerta, Lara; Taylor, Julian S.; Cardona-Gómez, Gloria P.; Nieto-Sampedro, Manuel; Martínez-Murillo, Ricardo
2012-01-01
The increased risk and prevalence of lacunar stroke and Parkinson's disease (PD) makes the search for better experimental models an important requirement for translational research. In this study we assess ischemic damage of the nigrostriatal pathway in a model of lacunar stroke evoked by damaging the perforating arteries in the territory of the substantia nigra (SN) of the rat after stereotaxic administration of endothelin-1 (ET-1), a potent vasoconstrictor peptide. We hypothesized that transplantation of neural stem cells (NSCs) with the capacity of differentiating into diverse cell types such as neurons and glia, but with limited proliferation potential, would constitute an alternative and/or adjuvant therapy for lacunar stroke. These cells showed neuritogenic activity in vitro and a high potential for neural differentiation. Light and electron microscopy immunocytochemistry was used to characterize GFP-positive neurons derived from the transplants. 48 h after ET-1 injection, we characterized an area of selective degeneration of dopaminergic neurons within the nigrostriatal pathway characterized with tissue necrosis and glial scar formation, with subsequent behavioral signs of Parkinsonism. Light microscopy showed that grafted cells within the striatal infarction zone differentiated with a high yield into mature glial cells (GFAP-positive) and neuron types present in the normal striatum. Electron microscopy revealed that NSCs-derived neurons integrated into the host circuitry establishing synaptic contacts, mostly of the asymmetric type. Astrocytes were closely associated with normal small-sized blood vessels in the area of infarct, suggesting a possible role in the regulation of the blood brain barrier and angiogenesis. Our results encourage the use of NSCs as a cell-replacement therapy for the treatment of human vascular Parkinsonism. PMID:22876219
Artificial neural networks for modeling time series of beach litter in the southern North Sea.
Schulz, Marcus; Matthies, Michael
2014-07-01
In European marine waters, existing monitoring programs of beach litter need to be improved concerning litter items used as indicators of pollution levels, efficiency, and effectiveness. In order to ease and focus future monitoring of beach litter on few important litter items, feed-forward neural networks consisting of three layers were developed to relate single litter items to general categories of marine litter. The neural networks developed were applied to seven beaches in the southern North Sea and modeled time series of five general categories of marine litter, such as litter from fishing, shipping, and tourism. Results of regression analyses show that general categories were predicted significantly moderately to well. Measured and modeled data were in the same order of magnitude, and minima and maxima overlapped well. Neural networks were found to be eligible tools to deliver reliable predictions of marine litter with low computational effort and little input of information. Copyright © 2014 Elsevier Ltd. All rights reserved.
D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process
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Shu-zhi Gao
2014-01-01
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Elman neural network for modeling and predictive control of delayed dynamic systems
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Wysocki Antoni
2016-03-01
Full Text Available The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
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Wei He
2013-01-01
Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.
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ZHANG Yongzhi
2016-10-01
Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.
May, D; Sivakumar, M
2008-01-01
Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.
Testolin, Alberto; Zorzi, Marco
2016-01-01
Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.
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Alberto Testolin
2016-07-01
Full Text Available Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.
Backpropagation Neural Network Modeling for Fault Location in Transmission Line 150 kV
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Azriyenni Narwan
2014-03-01
Full Text Available In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
Biological modelling of a computational spiking neural network with neuronal avalanches
Li, Xiumin; Chen, Qing; Xue, Fangzheng
2017-05-01
In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.
Metamizole Sodium Induces Neural Tube Defects in a Chick Embryo Model.
Guvenc, Yahya; Billur, Deniz; Aydin, Sevim; Ozeren, Ersin; Demirci, Adnan; Alagoz, Fatih; Dalgic, Ali; Belen, Deniz
The aim of this study was to investigate the effects of metamizole sodium on neural tube development in the early stage chick embryo model that complies with the first month of embryonic development in mammals. A total of 40 fertilized chicken eggs were divided into 4 equal groups. The eggs were incubated in the incubator at a temperature of 37.8±2°C with 60±5% humidity. Group A was the control, Group B was administered physiological saline, Group C was administered 30 mg/kg metamizole sodium (based on the therapeutic index range of it used in humans) and Group D was administered 90 mg/kg metamizole sodium. All embryos were removed from the egg at the 48th hour and morphologically and histologically examined. Normal development was seen and the neural tube was closed in 17 embryos in Groups A and B. A neural tube defect was seen in 2 embryos in group A and in 1 embryo in group B. A neural tube closure defect was seen in all embryos in group C and 9 embryos in group D. There was 1 dead embryo in Group D. Metamizole sodium was seen to produce a neural tube defect in the chicken embyro model.
Nasertdinova, A. D.; Bochkarev, V. V.
2017-11-01
Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.
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F. J. Barbero
2006-09-01
Full Text Available In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain, a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA, a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%. The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%. This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.
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Stefan Fürtinger
2014-11-01
Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number
Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun
2017-08-01
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.
Nakano, Takashi; Otsuka, Makoto; Yoshimoto, Junichiro; Doya, Kenji
2015-01-01
A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach.
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Marijana Zekić-Sušac
2013-02-01
Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.
Yorek, Nurettin; Ugulu, Ilker
2015-01-01
In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…
An avian model for the reversal of neurobehavioral teratogenicity with neural stem cells.
Dotan, Sharon; Pinkas, Adi; Slotkin, Theodore A; Yanai, Joseph
2010-01-01
A fast and simple model which uses lower animals on the evolutionary scale is beneficial for developing procedures for the reversal of neurobehavioral teratogenicity with neural stem cells. Here, we established a procedure for the derivation of chick neural stem cells, establishing embryonic day (E) 10 as optimal for progression to neuronal phenotypes. Cells were obtained from the embryonic cerebral hemispheres and incubated for 5-7 days in enriched medium containing epidermal growth factor (EGF) and basic fibroblast growth factor (FGF2) according to a procedure originally developed for mice. A small percentage of the cells survived, proliferated and formed nestin-positive neurospheres. After removal of the growth factors to allow differentiation (5 days), 74% of the cells differentiated into all major lineages of the nervous system, including neurons (Beta III tubulin-positive, 54% of the total number of differentiated cells), astrocytes (GFAP-positive, 26%), and oligodendrocytes (O4-positive, 20%). These findings demonstrate that the cells were indeed neural stem cells. Next, the cells were transplanted in two allograft chick models; (1) direct cerebral transplantation to 24-h-old chicks, followed by post-transplantation cell tracking at 24 h, 6 days and 14 days, and (2) intravenous transplantation to chick embryos on E13, followed by cell tracking on E19. With both methods, transplanted cells were found in the brain. The chick embryo provides a convenient, precisely-timed and unlimited supply of neural progenitors for therapy by transplantation, as well as constituting a fast and simple model in which to evaluate the ability of neural stem cell transplantation to repair neural damage, steps that are critical for progress toward therapeutic applications. Copyright 2010 Elsevier Inc. All rights reserved.
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Hamid R. Khosravani
2016-01-01
Full Text Available Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.
Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover
Directory of Open Access Journals (Sweden)
Yueru Ma
2014-01-01
Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.
The necessity of connection structures in neural models of variable binding
van der Velde, Frank; van der Velde, Frank; de Kamps, Marc
2015-01-01
In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1–11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other (‘connectivity based’) type uses dedicated connections structures to
A novel soft sensor model based on artificial neural network in the ...
African Journals Online (AJOL)
Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor ...
Neural Networks to model the innovativeness perception of co-creative firms
DEFF Research Database (Denmark)
Tanev, Stoyan
2012-01-01
contribution is to make a quantitative analysis in order to assess the relationship between value co-creation and innovation in technology-driven ﬁrms: we are using Artiﬁcial Neural Network (ANN) to investigate the relationship between value co-creation and innovativeness, and Self Organising Map (SOM) models...
Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
2011-01-01
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…
Artificial neural network modeling of DDGS flowability with varying process and storage parameters
Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....
Susan L. King
2003-01-01
The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...
Oscillatory Behavior on a Three-Node Neural Network Model with Discrete and Distributed Delays
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Chunhua Feng
2014-01-01
Full Text Available This paper investigates the oscillatory behavior of the solutions for a three-node neural network with discrete and distributed delays. Two theorems are provided to determine the conditions for oscillating solutions of the model. The criteria for selecting the parameters in this network are derived. Some simulation examples are presented to illustrate the effectiveness of the results.
Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions
Directory of Open Access Journals (Sweden)
Ganapathy Thirunavukkarasu
2009-01-01
Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.
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Manjunath Patel Gowdru Chandrashekarappa
2014-01-01
Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.
Neural networks in high-performance liquid chromatography optimization : Response surface modeling
Metting, H.J; Coenegracht, P.M J
1996-01-01
The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method
Comparison of different computer models of the neural control system of the lower urinary tract
van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.
2000-01-01
This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of
Travelling waves in models of neural tissue: from localised structures to periodic waves
Meijer, Hil Gaétan Ellart; Coombes, Stephen
2014-01-01
We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength
A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling
Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo
1996-01-01
The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.
Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Energy Technology Data Exchange (ETDEWEB)
Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)
2014-03-15
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.
Multiobjective training of artificial neural networks for rainfall-runoff modeling
De Vos, N.J.; Rientjes, T.H.M.
2008-01-01
This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for
Modeling fMRI signals can provide insights into neural processing in the cerebral cortex
Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo
2015-01-01
Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586
Fuzzified Data Based Neural Network Modeling for Health Assessment of Multistorey Shear Buildings
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Deepti Moyi Sahoo
2013-01-01
Full Text Available The present study intends to propose identification methodologies for multistorey shear buildings using the powerful technique of Artificial Neural Network (ANN models which can handle fuzzified data. Identification with crisp data is known, and also neural network method has already been used by various researchers for this case. Here, the input and output data may be in fuzzified form. This is because in general we may not get the corresponding input and output values exactly (in crisp form, but we have only the uncertain information of the data. This uncertain data is assumed in terms of fuzzy number, and the corresponding problem of system identification is investigated.
Foreground removal from Planck Sky Model temperature maps using a MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik; Hebert, K.
2009-01-01
with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....
Directory of Open Access Journals (Sweden)
C. K. Kwong
2013-01-01
Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
A neural joint model for entity and relation extraction from biomedical text.
Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong
2017-03-31
Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.
Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
Applications of artificial neural networks for microbial water quality modeling
Energy Technology Data Exchange (ETDEWEB)
Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu
2002-06-15
There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)
Directory of Open Access Journals (Sweden)
Azarnoush Ansari
2016-06-01
Full Text Available The purpose of this study is to investigate the customer–service provider relationship in the insurance industry using artificial neural networks and linear regression. Using a sample of 389 customers from 10 different startup insurance companies, it was found that artificial neural networks are an efficient way to evaluate the factors affecting customer loyalty. The results indicated that customer satisfaction and perceived value are significant predictors of customer loyalty. Additionally, it was found that trust, perceived quality, and empathy have a significant impact on both customer satisfaction and perceived value. The results also showed that customer commitment to service provider is positively associated with customer satisfaction and loyalty. After comparing the performance of linear regression models with artificial neural networks, it was found that the use of neural networks is a better approach for analyzing the customer loyalty, satisfaction, and perceived value. The use of new techniques such as artificial neural networks for analyzing the customer behavior can be particularly beneficial for startup companies who aspire to gain competitive advantage over their strong and well-established rivals.
A linear model for characterization of synchronization frequencies of neural networks.
Lv, Peili; Hu, Xintao; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming
2014-02-01
The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.
STOCHASTIC MODELLING BASED MONTHLY RAINFALL PREDICTION USING SEASONAL ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
S.M. Karthik
2017-01-01
Full Text Available India is an agrarian society where 13.7% of GDP and 50% of workforce are involved with agriculture. Rainfall plays a vital role in irrigating the land and replenishing the rivers and underground water sources. Therefore the study of rainfall is vital to the economic development and wellbeing of the nation. Accurate prediction of rainfall leads to better agricultural planning, flood prevention and control. The seasonal artificial neural networks can predict monthly rainfall by exploiting the cyclical nature of the weather system. It is dependent on historical time series data and therefore independent of changes in the fundamental models of climate known collectively as manmade climate change. This paper presents the seasonal artificial neural networks applied on the prediction of monthly rainfall. The amounts of rainfall in the twelve months of a year are fed to the neural networks to predict the next twelve months. The gradient descent method is used for training the neural networks. Four performance measures viz. MSE, RMSE, MAD and MAPE are used to assess the system. Experimental results indicate that monthly rainfall patterns can be predicted accurately by seasonal neural networks.
Biological neural networks as model systems for designing future parallel processing computers
Ross, Muriel D.
1991-01-01
One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.
Neural Network modeling of forward and inverse behavior of rotary MR damper
DEFF Research Database (Denmark)
Bhowmik, Subrata; Høgsberg, Jan Becker; Weber, Felix
2010-01-01
Magneto-rheological (MR) dampers have received considerable attention within the last decades, mainly because of their design simplicity, low power requirements, large force range and robustness. The most common models to describe the dynamic MR damper behavior are the Bouc-Wen model, the Lu...... of nonlinear problems. The present paper concerns the nonparametric neural network modeling of the dynamic behavior of a rotary MR damper. A rotary type MR damper consists of a rotating disk which is enclosed in a metallic housing filled with the MR fluid which is operated in shear mode. The dissipative torque...... produced is transformed into a translational force through the crank shaft mechanism. A feed-forward back propagation neural network is used to model both the forward and the inverse dynamics of the MR damper. The forward model output is the estimated force and therefore can be used later as observer...
Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.
Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun
2016-07-03
ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.
Kriegeskorte, Nikolaus
2015-11-24
Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.
One Prediction Model Based on BP Neural Network for Newcastle Disease
Wang, Hongbin; Gong, Duqiang; Xiao, Jianhua; Zhang, Ru; Li, Lin
The purpose of this paper is to investigate the correlation between meteorological factors and Newcastle disease incidence, and to determine the key factors that affect Newcastle disease. Having built BP neural network forecasting model by Matlab 7.0 software, we tested the performance of the model according to the coefficient of determination (R2) and absolute values of the difference between predictive value and practical incidence. The result showed that 6 kinds of meteorological factors determined, and the model's coefficient of determination is 0.760, and the performance of the model is very good. Finally, we build Newcastle disease forecasting model, and apply BP neural network theory in animal disease forecasting research firstly.
Kozma, Robert
2016-01-01
This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.
Directory of Open Access Journals (Sweden)
Ruijing Gan
2016-11-01
Full Text Available This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA, generalized regression neural networks (GRNN, and wavelet neural networks (WNN. In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.
Butterfly Classification by HSI and RGB Color Models Using Neural Networks
Directory of Open Access Journals (Sweden)
Jorge E. Grajales-Múnera
2013-11-01
Full Text Available This study aims the classification of Butterfly species through the implementation of Neural Networks and Image Processing. A total of 9 species of Morpho genre which has blue as a characteristic color are processed. For Butterfly segmentation we used image processing tools such as: Binarization, edge processing and mathematical morphology. For data processing RGB values are obtained for every image which are converted to HSI color model to identify blue pixels and obtain the data to the proposed Neural Networks: Back-Propagation and Perceptron. For analysis and verification of results confusion matrix are built and analyzed with the results of neural networks with the lowest error levels. We obtain error levels close to 1% in classification of some Butterfly species.
2016-09-15
neural network using applications across varied industries . Alam et al. [2] showed the factorial design did not perform as well as other designs (mentioned...composite design with a neural network using applications across varied industries . Alam et al. [2] showed the central composite design did not perform as...ON IMPROVED LEAST SQUARES REGRESSION & ARTIFICIAL NEURAL NETWORK META-MODELS FOR SIMULATION VIA CONTROL VARIATES DISSERTATION Michael P. Gibb
Wei, Hui; Ren, Yuan; Wang, Zi Yan
2013-10-01
The implementation of Hubel-Wiesel hypothesis that orientation selectivity of a simple cell is based on ordered arrangement of its afferent cells has some difficulties. It requires the receptive fields (RFs) of those ganglion cells (GCs) and LGN cells to be similar in size and sub-structure and highly arranged in a perfect order. It also requires an adequate number of regularly distributed simple cells to match ubiquitous edges. However, the anatomical and electrophysiological evidence is not strong enough to support this geometry-based model. These strict regularities also make the model very uneconomical in both evolution and neural computation. We propose a new neural model based on an algebraic method to estimate orientations. This approach synthesizes the guesses made by multiple GCs or LGN cells and calculates local orientation information subject to a group of constraints. This algebraic model need not obey the constraints of Hubel-Wiesel hypothesis, and is easily implemented with a neural network. By using the idea of a satisfiability problem with constraints, we also prove that the precision and efficiency of this model are mathematically practicable. The proposed model makes clear several major questions which Hubel-Wiesel model does not account for. Image-rebuilding experiments are conducted to check whether this model misses any important boundary in the visual field because of the estimation strategy. This study is significant in terms of explaining the neural mechanism of orientation detection, and finding the circuit structure and computational route in neural networks. For engineering applications, our model can be used in orientation detection and as a simulation platform for cell-to-cell communications to develop bio-inspired eye chips.
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling
Directory of Open Access Journals (Sweden)
F. C. Corazza
2005-03-01
Full Text Available Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with StatisticaÒ were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1 noncompetitive inhibition by substrate and competitive by product and 2 uncompetitive inhibition by substrate and competitive by product; however, these models’ correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.
Directory of Open Access Journals (Sweden)
J. C. Ochoa-Rivera
2002-01-01
Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..
Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model.
Xie, Fengying; Fan, Haidi; Li, Yang; Jiang, Zhiguo; Meng, Rusong; Bovik, Alan
2017-03-01
We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, features descriptive of tumor color, texture and border are extracted; and third, lesion objects are classified using a classifier based on a neural network ensemble model. In clinical situations, lesions occur that are too large to be entirely contained within the dermoscopy image. To deal with this difficult presentation, new border features are proposed, which are able to effectively characterize border irregularities on both complete lesions and incomplete lesions. In our model, a network ensemble classifier is designed that combines back propagation (BP) neural networks with fuzzy neural networks to achieve improved performance. Experiments are carried out on two diverse dermoscopy databases that include images of both the xanthous and caucasian races. The results show that classification accuracy is greatly enhanced by the use of the new border features and the proposed classifier model.
Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model
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Yihong Wang
2017-01-01
Full Text Available Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors.
Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels
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Antonino Laudani
2015-01-01
Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.
Chen, Chang Hao; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Klug, Achim; Lei, Tim C
2014-01-01
Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.
Directory of Open Access Journals (Sweden)
Chang Hao Chen
2014-01-01
Full Text Available Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.
Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus
2017-01-01
Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.
Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model.
Wang, Yihong; Wang, Rubin; Xu, Xuying
2017-01-01
Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors.
Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.
Latteri, Alberta; Arena, Paolo; Mazzone, Paolo
2011-04-15
Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort
NONLINEAR SYSTEM MODELING USING SINGLE NEURON CASCADED NEURAL NETWORK FOR REAL-TIME APPLICATIONS
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S. Himavathi
2012-04-01
Full Text Available Neural Networks (NN have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationally less complex. In this paper a parametric method of analysis is adopted to determine the compact and faster NN model among various neural network architectures. This work proves through analysis and examples that the Single Neuron Cascaded (SNC architecture is distinct in providing compact and simpler models requiring lower execution time. The unique structural growth of SNC architecture enables automation in design. The SNC Network is shown to combine the advantages of both single and multilayer neural network architectures. Extensive analysis on selected architectures and their models for four benchmark nonlinear theoretical plants and a practical application are tested. A performance comparison of the NN models is presented to demonstrate the superiority of the single neuron cascaded architecture for online real time applications.
Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation
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Chunqing Li
2012-01-01
Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.
Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling
Mohamadian, Masoumeh; Afarideh, Hossein; Ghergherehchi, Mitra
2017-01-01
The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors’ optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.
Pradhan, B.; Buchroithner, M. F.; Mansor, S.
2009-04-01
This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are nine landslide related factors were extracted from the spatial database and the neural network, frequency ratio and logistic regression coefficients of each factor was computed. Landslide susceptibility maps were drawn for study area using neural network, frequency ratios and logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that frequency ratio model provides higher prediction accuracy than the ANN and regression models.
Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.
Honarmand, M; Mousavi, S M
2014-04-01
Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.
Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
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Jianjin Wang
2017-01-01
Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.
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Beigi Mohsen
2017-01-01
Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.
A decision-making model based on a spiking neural circuit and synaptic plasticity.
Wei, Hui; Bu, Yijie; Dai, Dawei
2017-10-01
To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.
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Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
Kaiser, K L E; Niculescu, S P; Schultz, T W
2002-03-01
We present the results of an investigation into the use of a probabilistic neural network (PNN) based methodology to model the 48-60-h ICG50 (inhibitory concentration for population growth) sublethal toxicity to the ciliate Tetrahymena pyriformis. The information fed into the neural network is solely based on simple molecular descriptors as can be derived from the chemical structure. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as an input parameter and no rules of thumb, or other substance selection-criteria, are involved. The model was trained on a 1,000 substances data set and validated using an 84 substances external test set. The associated analysis of errors confirms the excellent recognitive and predictive capabilities of the model.
[Research on UKF control of epileptic-form spikes in neural mass models].
Liu, Xian; Ma, Baiwang; Ji, June; Li, Xiaoli
2013-12-01
Neural mass models are able to produce epileptic electroencephalogram (EEG) signals in different stages of seizures. The models play important roles in studying the mechanism analysis and control of epileptic seizures. In this study, the closed-loop feedback control was used to suppress the epileptic-form spikes in the neural mass models. It was expected to provide certain theory basis for the choice of stimulus position and parameter in the clinical treatment. With the influence of measurement noise taken into account, an unscented Kalman filter (UKF) was added to the feedback loop to estimate the system state and an UKF controller was constructed via the estimated state. The control action was imposed on the hyper-excitable population and all populations respectively in simulations. It was shown that both UKF control schemes suppressed the epileptic-form spikes in the model. However, the control energy needed in the latter scheme was less than that needed in the former one.
CSIR Research Space (South Africa)
Miya, WS
2008-10-01
Full Text Available In this paper, a comparison between Extension Neural Network (ENN), Gaussian Mixture Model (GMM) and Hidden Markov model (HMM) is conducted for bushing condition monitoring. The monitoring process is a two-stage implementation of a classification...
Directory of Open Access Journals (Sweden)
Hyun-Joo Oh
2017-09-01
Full Text Available The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN and boosted tree (BT, for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.
Equation-oriented specification of neural models for simulations
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Marcel eStimberg
2014-02-01
Full Text Available Simulating biological neuronal networks is a core method of research in computational neuroscience. A full specification of such a network model includes a description of the dynamics and state changes of neurons and synapses, as well as the synaptic connectivity patterns and the initial values of all parameters. A standard approach in neuronal modelling software is to build models based on a library of pre-defined models and mechanisms; if a model component does not yet exist, it has to be defined in a special-purpose or general low-level language and potentially be compiled and linked with the simulator. Here we propose an alternative approach that allows flexible definition of models by writing textual descriptions based on mathematical notation. We demonstrate that this approach allows the definition of a wide range of models with minimal syntax. Furthermore, such explicit model descriptions allow the generation of executable code for various target languages and devices, since the description is not tied to an implementation. Finally, this approach also has advantages for readability and reproducibility, because the model description is fully explicit, and because it can be automatically parsed and transformed into formatted descriptions.The presented approach has been implemented in the Brian2 simulator.
N-gram Language Modeling using Recurrent Neural Network Estimation
Chelba, Ciprian; Norouzi, Mohammad; Bengio, Samy
2017-01-01
We investigate the effective memory depth of RNN models by using them for $n$-gram language model (LM) smoothing. Experiments on a small corpus (UPenn Treebank, one million words of training data and 10k vocabulary) have found the LSTM cell with dropout to be the best model for encoding the $n$-gram state when compared with feed-forward and vanilla RNN models. When preserving the sentence independence assumption the LSTM $n$-gram matches the LSTM LM performance for $n=9$ and slightly outperfo...
Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network
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D. Peraković
2017-06-01
Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.
Beyond the quantum formalism: consequences of a neural-oscillator model to quantum cognition
de Barros, J Acacio
2013-01-01
In this paper we present a neural oscillator model of stimulus response theory that exhibits quantum-like behavior. We then show that without adding any additional assumptions, a quantum model constructed to fit observable pairwise correlations has no predictive power over the unknown triple moment, obtainable through the activation of multiple oscillators. We compare this with the results obtained in de Barros (2013), where a criteria of rationality gives optimal ranges for the triple moment.
The Vite Model: A Neural Command Circuit for Generating Arm and Articulator Trajectories,
1988-03-01
associative map, looking at an object can activate a TPC of the hand-arm system, as Piaget (1963) noted. Then a VITE circuit can translate this latter TPC...two ways: by comparing trajectories of the neural circuit’s output stage with actual arm trajectories, and by checking for the existence of the...in precentral motor cortex could be analysed as an in vivo analogue of model DV stage neurons. Additional physiological support for the VITE model
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Glenski, Maria; Ayton, Ellyn; Arendt, Dustin; Volkova, Svitlana
2017-01-01
This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts. Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavi...
Hermantoro; Rudiyanto; Slamet Suprayogi
2008-01-01
Land evaluation for specific purpose in plantation sector become very important due to increasing the competition in land use and the development of plantation sector. Land evaluation produces information of land economic values for specific land use. The objective of the research is to develop land evaluation method for cocoa estate using integrated model Artificial Neural Network (ANN) and Geographical Information System (GIS). Back propagation ANN model were used to predict cocoa yield bas...
Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach
Di Fenza, Armida; Alagona, Giuliano; Ghio, Caterina; Leonardi, Riccardo; Giolitti, Alessandro; Madami, Andrea
2007-04-01
The ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN). Our implementation of neural network models for the prediction of intestinal absorption is based on the correlation of Caco-2 cell apparent permeability ( P app) values, as a measure of intestinal absorption, to the structures of two different data sets of drug candidates. Several molecular descriptors of the compounds were calculated and the optimal subsets were selected using a genetic algorithm; therefore, the method was indicated as Genetic Algorithm-Neural Network (GA-NN). A methodology combining a genetic algorithm search with neural network analysis applied to the modelling of Caco-2 P app has never been presented before, although the two procedures have been already employed separately. Moreover, we provide new Caco-2 cell permeability measurements for more than two hundred compounds. Interestingly, the selected descriptors show to possess physico-chemical connotations which are in excellent accordance with the well known relevant molecular properties involved in the cellular membrane permeation phenomenon: hydrophilicity, hydrogen bonding propensity, hydrophobicity and molecular size. The predictive ability of the models, although rather good for a preliminary study, is somewhat affected by the poor precision of the experimental Caco-2 measurements. Finally, the generalization ability of one model was checked on an external test set not derived from the data sets used to build the models
Recurrent neural network modeling of nearshore sandbar behavior
Pape, Leo; Ruessink, B.G.; Wiering, Marco A.; Turner, Ian L.
2007-01-01
The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input,
An integrative neural model of social perception, action observation, and theory of mind.
Yang, Daniel Y-J; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A
2015-04-01
In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. Copyright © 2015 Elsevier Ltd. All rights reserved.
Feed-forward neural network model for hunger and satiety related VAS score prediction.
Krishnan, Shaji; Hendriks, Henk F J; Hartvigsen, Merete L; de Graaf, Albert A
2016-07-07
An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions. The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms. From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.
Neural networks-based modeling applied to a process of heavy metals removal from wastewaters.
Suditu, Gabriel D; Curteanu, Silvia; Bulgariu, Laura
2013-01-01
This article approaches the problem of environment pollution with heavy metals from disposal of industrial wastewaters, namely removal of these metals by means of biosorbents, particularly with Romanian peat (from Poiana Stampei). The study is carried out by simulation using feed-forward and modular neural networks with one or two hidden layers, pursuing the influence of certain operating parameters (metal nature, sorbent dose, pH, temperature, initial concentration of metal ion, contact time) on the amount of metal ions retained on the unit mass of sorbent. In neural network modeling, a consistent data set was used, including five metals: lead, mercury, cadmium, nickel and cobalt, the quantification of the metal nature being done by its electronegativity. Even if based on successive trials, the method of designing neural models was systematically conducted, recording and comparing the errors obtained with different types of neural networks, having various numbers of hidden layers and neurons, number of training epochs, or using various learning methods. The errors with values under 5% make clear the efficiency of the applied method.
Model Building and Optimization Analysis of MDF Continuous Hot-Pressing Process by Neural Network
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Qingfa Li
2016-01-01
Full Text Available We propose a one-layer neural network for solving a class of constrained optimization problems, which is brought forward from the MDF continuous hot-pressing process. The objective function of the optimization problem is the sum of a nonsmooth convex function and a smooth nonconvex pseudoconvex function, and the feasible set consists of two parts, one is a closed convex subset of Rn, and the other is defined by a class of smooth convex functions. By the theories of smoothing techniques, projection, penalty function, and regularization term, the proposed network is modeled by a differential equation, which can be implemented easily. Without any other condition, we prove the global existence of the solutions of the proposed neural network with any initial point in the closed convex subset. We show that any accumulation point of the solutions of the proposed neural network is not only a feasible point, but also an optimal solution of the considered optimization problem though the objective function is not convex. Numerical experiments on the MDF hot-pressing process including the model building and parameter optimization are tested based on the real data set, which indicate the good performance of the proposed neural network in applications.
An integrative neural model of social perception, action observation, and theory of mind
Yang, Daniel Y.-J.; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A.
2016-01-01
In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. PMID:25660957
A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System
Directory of Open Access Journals (Sweden)
Metin Demirtas
2011-07-01
Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.
Renewal-process approximation of a stochastic threshold model for electrical neural stimulation.
Bruce, I C; Irlicht, L S; White, M W; O'Leary, S J; Clark, G M
2000-01-01
In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.
Data on copula modeling of mixed discrete and continuous neural time series
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Meng Hu
2016-06-01
Full Text Available Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula” [1]. Here we present further data for joint analysis of spike and local field potential (LFP with copula modeling. In particular, the details of different model orders and the influence of possible spike contamination in LFP data from the same and different electrode recordings are presented. To further facilitate the use of our copula model for the analysis of mixed data, we provide the Matlab codes, together with example data.
Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon
2017-07-03
Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.
Neural underpinnings of decision strategy selection: a review and a theoretical model
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Szymon Wichary
2016-11-01
Full Text Available In multi-attribute choice, decision makers use various decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a unifying neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g. affect, stress on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models explaining this process. We also present the neurocognitive Bottom-Up Model of Strategy Selection (BUMSS. The model assumes that the use of the rational, normative Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: 1 cue weight computation, 2 gain modulation, and 3 weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neurophysiological indices.
Real-time process optimization based on grey-box neural models
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F. A. Cubillos
2007-09-01
Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.
A neural network based model to analyze rice parboiling process with small dataset.
Behroozi-Khazaei, Nasser; Nasirahmadi, Abozar
2017-07-01
In this study, milling recovery, head rice yield, degree of milling and whiteness were utilized to characterize the milling quality of Tarom parboiled rice variety. The parboiled rice was prepared with three soaking temperatures and steaming times. Then the samples were dried to three levels of final moisture contents [8, 10 and 12% (w.b)]. Modeling of process and validating of the results with small dataset are always challenging. So, the aim of this study was to develop models based on the milling quality data in parboiling process by means of multivariate regression and artificial neural network. In order to validate the neural network model with a little dataset, K-fold cross validation method was applied. The ANN structure with one hidden layer and Tansig transfer function by 18 neurons in the hidden layer was selected as the best model in this study. The results indicated that the neural network could model the parboiling process with higher degree of accuracy. This method was a promising procedure to create accuracy and can be used as a reliable model to select the best parameters for the parboiling process with little experiment dataset.
A model for integrating elementary neural functions into delayed-response behavior.
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Thomas Gisiger
2006-04-01
Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.
Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.
2013-07-01
Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.
Vos, JE; vanHeijst, JJ; Greuters, S; Silva, FL; Principe, JC; Almeida, LB
1997-01-01
An artificial neural network model is presented in which the development is simulated of a baby's ability to control movement of his forearm around the elbow, until he is capable of goal-directed reaching. The neural network implementation provides the facility to change the number of nodes (or
An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...
DEFF Research Database (Denmark)
Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T
2011-01-01
Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral...
Neural models on temperature regulation for cold-stressed animals
Horowitz, J. M.
1975-01-01
The present review evaluates several assumptions common to a variety of current models for thermoregulation in cold-stressed animals. Three areas covered by the models are discussed: signals to and from the central nervous system (CNS), portions of the CNS involved, and the arrangement of neurons within networks. Assumptions in each of these categories are considered. The evaluation of the models is based on the experimental foundations of the assumptions. Regions of the nervous system concerned here include the hypothalamus, the skin, the spinal cord, the hippocampus, and the septal area of the brain.
Dropka, Natasha; Holena, Martin
2017-08-01
In directional solidification of silicon, the solid-liquid interface shape plays a crucial role for the quality of crystals. The interface shape can be influenced by forced convection using travelling magnetic fields. Up to now, there is no general and explicit methodology to identify the relation and the optimum combination of magnetic and growth parameters e.g., frequency, phase shift, current magnitude and interface deflection in a buoyancy regime. In the present study, 2D CFD modeling was used to generate data for the design and training of artificial neural networks and for Gaussian process modeling. The aim was to quickly assess the complex nonlinear dependences among the parameters and to optimize them for the interface flattening. The first encouraging results are presented and the pros and cons of artificial neural networks and Gaussian process modeling discussed.
Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response
Energy Technology Data Exchange (ETDEWEB)
Edelen, Auralee [Fermilab; Biedron, Sandra [Colorado State U., Fort Collins; Bowring, Daniel [Fermilab; Chase, Brian [Fermilab; Edelen, Jonathan [Fermilab; Milton, Stephen [Colorado State U., Fort Collins; Steimel, Jim [Fermilab
2016-06-01
As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.
Maulidah, Rifa'atul; Purqon, Acep
2016-08-01
Mendong (Fimbristylis globulosa) has a potentially industrial application. We investigate a predictive model for heat and mass transfer in drying kinetics during drying a Mendong. We experimentally dry the Mendong by using a microwave oven. In this study, we analyze three mathematical equations and feed forward neural network (FNN) with back propagation to describe the drying behavior of Mendong. Our results show that the experimental data and the artificial neural network model has a good agreement and better than a mathematical equation approach. The best FNN for the prediction is 3-20-1-1 structure with Levenberg- Marquardt training function. This drying kinetics modeling is potentially applied to determine the optimal parameters during mendong drying and to estimate and control of drying process.
Application of artificial neural networks for response surface modelling in HPLC method development
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Mohamed A. Korany
2012-01-01
Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.