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.
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.
DEFF Research Database (Denmark)
Hørning, Annette
1994-01-01
Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....
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.
Neto, João Pedro; Costa, José Félix
1999-01-01
In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...
Texture Based Image Analysis With Neural Nets
Ilovici, Irina S.; Ong, Hoo-Tee; Ostrander, Kim E.
1990-03-01
In this paper, we combine direct image statistics and spatial frequency domain techniques with a neural net model to analyze texture based images. The resultant optimal texture features obtained from the direct and transformed image form the exemplar pattern of the neural net. The proposed approach introduces an automated texture analysis applied to metallography for determining the cooling rate and mechanical working of the materials. The results suggest that the proposed method enhances the practical applications of neural nets and texture extraction features.
Khan, A M; Lee, Y K; Kim, T S
2008-01-01
Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.
From image edges to geons to viewpoint-invariant object models: a neural net implementation
Biederman, Irving; Hummel, John E.; Gerhardstein, Peter C.; Cooper, Eric E.
1992-03-01
Three striking and fundamental characteristics of human shape recognition are its invariance with viewpoint in depth (including scale), its tolerance of unfamiliarity, and its robustness with the actual contours present in an image (as long as the same convex parts [geons] can be activated). These characteristics are expressed in an implemented neural network model (Hummel & Biederman, 1992) that takes a line drawing of an object as input and generates a structural description of geons and their relations which is then used for object classification. The model's capacity for structural description derives from its solution to the dynamic binding problem of neural networks: independent units representing an object's parts (in terms of their shape attributes and interrelations) are bound temporarily when those attributes occur in conjunction in the system's input. Temporary conjunctions of attributes are represented by synchronized activity among the units representing those attributes. Specifically, the model induces temporal correlation in the firing of activated units to: (1) parse images into their constituent parts; (2) bind together the attributes of a part; and (3) determine the relations among the parts and bind them to the parts to which they apply. Because it conjoins independent units temporarily, dynamic binding allows tremendous economy of representation, and permits the representation to reflect an object's attribute structure. The model's recognition performance conforms well to recent results from shape priming experiments. Moreover, the manner in which the model's performance degrades due to accidental synchrony produced by an excess of phase sets suggests a basis for a theory of visual attention.
The process of learning in neural net models with Poisson and Gauss connectivities.
Sivridis, L; Kotini, A; Anninos, P
2008-01-01
In this study we examined the dynamic behavior of isolated and non-isolated neural networks with chemical markers that follow a Poisson or Gauss distribution of connectivity. The Poisson distribution shows higher activity in comparison to the Gauss distribution although the latter has more connections that obliterated due to randomness. We examined 57 hematoxylin and eosin stained sections from an equal number of autopsy specimens with a diagnosis of "cerebral matter within normal limits". Neural counting was carried out in 5 continuous optic fields, with the use of a simple optical microscope connected to a computer (software programmer Nikon Act-1 vers-2). The number of neurons that corresponded to a surface was equal to 0.15 mm(2). There was a gradual reduction in the number of neurons as age increased. A mean value of 45.8 neurons /0.15 mm(2) was observed within the age range 21-25, 33 neurons /0.15 mm(2) within the age range 41-45, 19.3 neurons /0.15 mm(2) within the age range 56-60 years. After the age of 60 it was observed that the number of neurons per unit area stopped decreasing. A correlation was observed between these experimental findings and the theoretical neural model developed by professor Anninos and his colleagues. Equivalence between the mean numbers of neurons of the above mentioned age groups and the highest possible number of synaptic connections per neuron (highest number of synaptic connections corresponded to the age group 21-25) was created. We then used both inhibitory and excitatory post-synaptic potentials and applied these values to the Poisson and Gauss distributions, whereas the neuron threshold was varied between 3 and 5. According to the obtained phase diagrams, the hysteresis loops decrease as age increases. These findings were significant as the hysteresis loops can be regarded as the basis for short-term memory.
Beyond Pattern Recognition With Neural Nets
Arsenault, Henri H.; Macukow, Bohdan
1989-02-01
Neural networks are finding many areas of application. Although they are particularly well-suited for applications related to associative recall such as content-addressable memories, neural nets can perform many other applications ranging from logic operations to the solution of optimization problems. The training of a recently introduced model to perform boolean logical operations such as XOR is described. Such simple systems can be combined to perform any complex boolean operation. Any complex task consisting of parallel and serial operations including fuzzy logic that can be described in terms of input-output relations can be accomplished by combining modules such as the ones described here. The fact that some modules can carry out their functions even when their inputs contain erroneous data, and the fact that each module can carry out its functions in parallel with itself and other modules promises some interesting applications.
CDMA and TDMA based neural nets.
Herrero, J C
2001-06-01
CDMA and TDMA telecommunication techniques were established long time ago, but they have acquired a renewed presence due to the rapidly increasing mobile phones demand. In this paper, we are going to see they are suitable for neural nets, if we leave the concept "connection" between processing units and we adopt the concept "messages" exchanged between them. This may open the door to neural nets with a higher number of processing units and flexible configuration.
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to
Neural Nets for Scene Analysis
1992-09-01
decision boundaries produced for the arificial database when prototypes are Se- feature 1 lected from reduced training set. ly selected from the 383...CLASSIFIER HIT MISS MOPOGIA CORRELATION LOW-LEVEL VISION IVARL&MCE NEURAL NE. (O D ILER) SE CORRELATION REUCE ETC.(OR I F RS)DI4ENSIONAIM AND TRAINING...A) = J11’, + tOi2Z2 + 61311’ (4) SPE Vol. 1608 mitalwg’t Robots and Coniutef Vision X (991)/501 - "X,, ,v ) X 1112 1P Pa P2 P2 .. 2 33 CL AS INPUT
Document analysis with neural net circuits
Graf, Hans Peter
1994-01-01
Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.
Real-time applications of neural nets
Energy Technology Data Exchange (ETDEWEB)
Spencer, J.E.
1989-05-01
Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.
Accelerator diagnosis and control by Neural Nets
Energy Technology Data Exchange (ETDEWEB)
Spencer, J.E.
1989-01-01
Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.
Geraldo Ferreira, A.; Lopez-Baeza, Ernesto; Velazquez Blazquez, Almudena; Soria-Olivas, Emilio; Serrano Lopez, Antonio J.; Gomez Chova, Juan
2012-07-01
In this work, Linear Models (LM) and Artificial Neural Networks (ANN) have been developed to estimate net radiation (RN) at the surface. The models have been developed and evaluated by using the synergy between Geostationary Earth Radiation Budget (GERB-1) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, both instruments onboard METEOSAT-9, and ``in situ'' measurements. The data used in this work, corresponding to August 2006 and June to August 2007, proceed from Top of the Atmosphere (TOA) broadband fluxes from GERB-1, every 15 min, and from net radiation at the surface measured, every 10 min, at the Valencia Anchor Station (VAS) area, having measured independently the shortwave and the longwave radiation components (downwelling and upwelling) for different land uses and land cover. The adjustment of both temporal resolutions for the satellite and in situ data was achieved by linear interpolation that showed less standard deviation than the cubic one. The LMs were developed and validated by using satellite TOA RN and ground station surface RN measurements, only considering cloudy free days selected from the ground data. The ANN model was developed both for cloudy and cloudy-free conditions using seven input variables selected for the training/validation sets, namely, hour, day, month, surface RN, solar zenith angle and TOA shortwave and longwave fluxes. Both, LMs and ANNs show remarkably good agreement when compared to surface RN measurements. Therefore, this methodology can be successfully applied to estimate RN at surface from GERB/SEVIRI data.
Webs, cell assemblies, and chunking in neural nets: introduction.
Wickelgren, W A
1999-03-01
This introduction to Wickelgren (1992), describes a theory of idea representation and learning in the cerebral cortex and seven properties of Hebb's (1949) formulation of cell assemblies that have played a major role in all such neural net models. Ideas are represented in the cerebral cortex by webs (innate cell assemblies), using sparse coding with sparse, all-or-none, innate linking. Recruiting a web to represent a new idea is called chunking. The innate links that bind the neurons of a web are basal dendritic synapses. Learning modifies the apical dendritic synapses that associate neurons in one web to neurons in another web.
22nd Italian Workshop on Neural Nets
Bassis, Simone; Esposito, Anna; Morabito, Francesco
2013-01-01
This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop- is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.
Optical neural net for classifying imaging spectrometer data
Barnard, Etienne; Casasent, David P.
1989-01-01
The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.
Classification of handwritten digits using a RAM neural net architecture
DEFF Research Database (Denmark)
Jørgensen, T.M.
1997-01-01
Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results...
Translating feedforward neural nets to SOM-like maps
van der Zwaag, B.J.; Spaanenburg, Lambert; Slump, Cornelis H.
A major disadvantage of feedforward neural networks is still the difficulty to gain insight into their internal functionality. This is much less the case for, e.g., nets that are trained unsupervised, such as Kohonen’s self-organizing feature maps (SOMs). These offer a direct view into the stored
Computation and control with neural nets
Energy Technology Data Exchange (ETDEWEB)
Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.
1989-10-04
As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.
Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications
Decker, Arthur J.
2004-01-01
Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.
The Development of Animal Behavior: From Lorenz to Neural Nets
Bolhuis, Johan J.
In the study of behavioral development both causal and functional approaches have been used, and they often overlap. The concept of ontogenetic adaptations suggests that each developmental phase involves unique adaptations to the environment of the developing animal. The functional concept of optimal outbreeding has led to further experimental evidence and theoretical models concerning the role of sexual imprinting in the evolutionary process of sexual selection. From a causal perspective it has been proposed that behavioral ontogeny involves the development of various kinds of perceptual, motor, and central mechanisms and the formation of connections among them. This framework has been tested for a number of complex behavior systems such as hunger and dustbathing. Imprinting is often seen as a model system for behavioral development in general. Recent advances in imprinting research have been the result of an interdisciplinary effort involving ethology, neuroscience, and experimental psychology, with a continual interplay between these approaches. The imprinting results are consistent with Lorenz' early intuitive suggestions and are also reflected in the architecture of recent neural net models.
ER fluid applications to vibration control devices and an adaptive neural-net controller
Morishita, Shin; Ura, Tamaki
1993-07-01
Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.
Fast neural net simulation with a DSP processor array.
Muller, U A; Gunzinger, A; Guggenbuhl, W
1995-01-01
This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications.
Artificial neural nets application in the cotton yarn industry
Directory of Open Access Journals (Sweden)
Gilberto Clóvis Antoneli
2016-06-01
Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction
Energy Technology Data Exchange (ETDEWEB)
Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.
2017-12-08
With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.
Perineuronal net, CSPG receptor and their regulation of neural plasticity.
Miao, Qing-Long; Ye, Qian; Zhang, Xiao-Hui
2014-08-25
Perineuronal nets (PNNs) are reticular structures resulting from the aggregation of extracellular matrix (ECM) molecules around the cell body and proximal neurite of specific population of neurons in the central nervous system (CNS). Since the first description of PNNs by Camillo Golgi in 1883, the molecular composition, developmental formation and potential functions of these specialized extracellular matrix structures have only been intensively studied over the last few decades. The main components of PNNs are hyaluronan (HA), chondroitin sulfate proteoglycans (CSPGs) of the lectican family, link proteins and tenascin-R. PNNs appear late in neural development, inversely correlating with the level of neural plasticity. PNNs have long been hypothesized to play a role in stabilizing the extracellular milieu, which secures the characteristic features of enveloped neurons and protects them from the influence of malicious agents. Aberrant PNN signaling can lead to CNS dysfunctions like epilepsy, stroke and Alzheimer's disease. On the other hand, PNNs create a barrier which constrains the neural plasticity and counteracts the regeneration after nerve injury. Digestion of PNNs with chondroitinase ABC accelerates functional recovery from the spinal cord injury and restores activity-dependent mechanisms for modifying neuronal connections in the adult animals, indicating that PNN is an important regulator of neural plasticity. Here, we review recent progress in the studies on the formation of PNNs during early development and the identification of CSPG receptor - an essential molecular component of PNN signaling, along with a discussion on their unique regulatory roles in neural plasticity.
Unfolding code for neutron spectrometry based on neural nets technology
Energy Technology Data Exchange (ETDEWEB)
Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)
2012-10-15
The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)
Thompson, D. E.; Rajkumar, T.
2002-12-01
The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Suisan Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Suisan Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located along the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay / Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with DSM2 is that the numerical simulation takes roughly 16 hours to complete a prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple gauging stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Suisan Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the Bay-Delta is strongly tidally driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.
Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan
2017-02-01
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.
Stability Training for Convolutional Neural Nets in LArTPC
Lindsay, Matt; Wongjirad, Taritree
2017-01-01
Convolutional Neural Nets (CNNs) are the state of the art for many problems in computer vision and are a promising method for classifying interactions in Liquid Argon Time Projection Chambers (LArTPCs) used in neutrino oscillation experiments. Despite the good performance of CNN's, they are not without drawbacks, chief among them is vulnerability to noise and small perturbations to the input. One solution to this problem is a modification to the learning process called Stability Training developed by Zheng et al. We verify existing work and demonstrate volatility caused by simple Gaussian noise and also that the volatility can be nearly eliminated with Stability Training. We then go further and show that a traditional CNN is also vulnerable to realistic experimental noise and that a stability trained CNN remains accurate despite noise. This further adds to the optimism for CNNs for work in LArTPCs and other applications.
Self-Organizing Neural-Net Control of Ship's Horizontal Motion
Energy Technology Data Exchange (ETDEWEB)
Yang, X J; Zhao, X R [Automation College of Harbin Engineering University, Harbin 150001 (China)
2006-10-15
This paper describes the concept and an example of an adaptive neural-net controller system for ship's horizontal motion. The system consists of two parts, a real-world part and an imaginary-world part. The real-world part is a feedback control system for the actual ship. In the imaginary-world part, the model of ship and the controller are adjusted continuously in order to deal with changes of dynamic properties caused by disturbances and so on. In this paper, the adaptability of the controller system is investigated by controlling ship's horizontal motion including roll, yaw and sway. The results of simulation indicate that with selforganizing neural-net control, the mean square error of roll angle and yaw angle reduce to 0.92{sup 0}, and 0.74{sup 0} respectively. The control effect of SONC is better than conventional LQG controller.
Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Ghosal, Sayan; Ray, Nilanjan
2016-01-01
Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the registration pipeline. The upper bound SSD (UB-SSD) enhances the original deformable model parameter space by adding a heatmap output from FCNN. Nex...
A taxonomy of Deep Convolutional Neural Nets for Computer Vision
Directory of Open Access Journals (Sweden)
Suraj eSrinivas
2016-01-01
Full Text Available Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs. We start with AlexNet'' as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.
Multilayer neural-net robot controller with guaranteed tracking performance.
Lewis, F L; Yegildirek, A; Liu, K
1996-01-01
A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.
Petri nets and other models of concurrency
DEFF Research Database (Denmark)
Nielsen, Mogens; Sassone, Vladimiro
1998-01-01
This paper retraces, collects, and summarises contributions of the authors - in collaboration with others - on the theme of Petri nets and their categorical relationships to other models of concurrency.......This paper retraces, collects, and summarises contributions of the authors - in collaboration with others - on the theme of Petri nets and their categorical relationships to other models of concurrency....
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. .
Geometrical approach to neural net control of movements and posture
Pellionisz, A. J.; Ramos, C. F.
1993-01-01
In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.
Directory of Open Access Journals (Sweden)
Egor Dzyubenko
2016-01-01
Full Text Available Synapses are specialized structures that mediate rapid and efficient signal transmission between neurons and are surrounded by glial cells. Astrocytes develop an intimate association with synapses in the central nervous system (CNS and contribute to the regulation of ion and neurotransmitter concentrations. Together with neurons, they shape intercellular space to provide a stable milieu for neuronal activity. Extracellular matrix (ECM components are synthesized by both neurons and astrocytes and play an important role in the formation, maintenance, and function of synapses in the CNS. The components of the ECM have been detected near glial processes, which abut onto the CNS synaptic unit, where they are part of the specialized macromolecular assemblies, termed perineuronal nets (PNNs. PNNs have originally been discovered by Golgi and represent a molecular scaffold deposited in the interface between the astrocyte and subsets of neurons in the vicinity of the synapse. Recent reports strongly suggest that PNNs are tightly involved in the regulation of synaptic plasticity. Moreover, several studies have implicated PNNs and the neural ECM in neuropsychiatric diseases. Here, we highlight current concepts relating to neural ECM and PNNs and describe an in vitro approach that allows for the investigation of ECM functions for synaptogenesis.
Development of a neural net paradigm that predicts simulator sickness
Energy Technology Data Exchange (ETDEWEB)
Allgood, G.O.
1993-03-01
A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.
k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification
Directory of Open Access Journals (Sweden)
Blaž Meden
2018-01-01
Full Text Available Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States); Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)
1995-12-01
This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.
Neural-net based real-time economic dispatch for thermal power plants
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)
1996-12-01
This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.
Investigation of neural-net based control strategies for improved power system dynamic performance
Energy Technology Data Exchange (ETDEWEB)
Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)
1995-12-31
The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.
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.
Deep Belief Nets for Topic Modeling
DEFF Research Database (Denmark)
Maaløe, Lars; Arngren, Morten; Winther, Ole
2015-01-01
-formative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep be-lief nets (DBN) that both find low-dimensional latent representations for documents....... Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an on-line publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance...
Fast neural-net based fake track rejection
De Cian, Michel; Seyfert, Paul; Stahl, Sascha
2017-01-01
A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, is fast enough to fit into the CPU time budget of the software trigger farm. It allows reducing the fake rate and consequently the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment.
A Petri Nets Model for Blockchain Analysis
Pinna, Andrea; Tonelli, Roberto; Orrú, Matteo; Marchesi, Michele
2017-01-01
A Blockchain is a global shared infrastructure where cryptocurrency transactions among addresses are recorded, validated and made publicly available in a peer- to-peer network. To date the best known and important cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency and in particular on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism. The proposed model allows us to quickly collect information about identities owning Bitcoin addresses and to re...
Intelligent control based on fuzzy logic and neural net theory
Lee, Chuen-Chien
1991-01-01
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system
Hanna, Moheb M.; Buck, A. A.; Smith, R.
1994-10-01
The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.
NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification
Directory of Open Access Journals (Sweden)
Min Peng
2016-10-01
Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.
Intelligent control aspects of fuzzy logic and neural nets
Harris, C J; Brown, M
1993-01-01
With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent expe
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...
Geraldo Ferreira, A.; Soria, Emilio; Lopez-Baeza, Ernesto; Vila, Joan; Serrano, Antonio J.; Martinez, Marcelino; Velazquez Blazquez, Almudena; Clerbaux, Nicolas
This paper describes the results obtained using Artificial Neural Networks (AAN) models to estimate the diurnal cycle of net radiation (Rn) at surface. The data used as input parameter in the AAN model were that measured by Geostationary Earth Radiation Budget (GERB-1) instrument, on board Meteosat 9 satellite. The data concerning Rn at the surface were collected at the Valencia Anchor Station (VAS), a ground reference meteorological station for the validation of low spatial resolution sensors situated near de city of Valencia, Spain. This data refers to the periods July 31st -August 6th 2006 and June 19th -August 18th 2007. Both, GERB-1 and VAS data are used to train and validate the AAN model. The same data set is also used to develop and validate a Multivariate Linear Regression (MLR) model. A comparison between the estimates provided by the AAN and the MLR models has been carried out; the results obtained with the neural model outperform the linear model. Moreover, the low values of the error indexes show that neural models can be used as an alternative methodology to make atmospheric corrections.
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...
Deep neural nets as a method for quantitative structure-activity relationships.
Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir
2015-02-23
Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.
Modeling safety requirements of an FMS using Petri-nets
Hanna, Moheb M.; Buck, A. A.; Smith, R.
1993-08-01
This paper is concerned with the modelling of safety requirements using Petri nets as a tool to model and simulate a Flexible Manufacturing System (FMS). The FMS cell described comprises a pick and place robot, a multi-head drilling machine together with a vision system and illustrates how the hierarchical structure of Petri nets can be used to ensure that all fail- safe requirements are satisfied; block diagrams together with fully detailed example Petri nets are given. The work demonstrates the use of cell and robot control Petro nets together with robot subnets for the x, y and z axes and associated output nets; the control and output nets are linked together with a safety net. Individual machines are linked with the control and safety nets of an FMS at cell level. The paper also illustrates how a Petri net can act as a decision maker during image inspection and identifies the unsafe conditions that can arise within an FMS.
Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades
Decker, Arthur J.
1999-01-01
Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.
Access Nets: Modeling Access to Physical Spaces
Frohardt, Robert; Chang, Bor-Yuh Evan; Sankaranarayanan, Sriram
Electronic, software-managed mechanisms using, for example, radio-frequency identification (RFID) cards, enable great flexibility in specifying access control policies to physical spaces. For example, access rights may vary based on time of day or could differ in normal versus emergency situations. With such fine-grained control, understanding and reasoning about what a policy permits becomes surprisingly difficult requiring knowledge of permission levels, spatial layout, and time. In this paper, we present a formal modeling framework, called AccessNets, suitable for describing a combination of access permissions, physical spaces, and temporal constraints. Furthermore, we provide evidence that model checking techniques are effective in reasoning about physical access control policies. We describe our results from a tool that uses reachability analysis to validate security policies.
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.
Sengupta, Sailik; Chakraborti, Tathagata; Kambhampati, Subbarao
2017-01-01
Recent works on gradient-based attacks and universal perturbations can adversarially modify images to bring down the accuracy of state-of-the-art classification techniques based on deep neural networks to as low as 10\\% on popular datasets like MNIST and ImageNet. The design of general defense strategies against a wide range of such attacks remains a challenging problem. In this paper, we derive inspiration from recent advances in the fields of cybersecurity and multi-agent systems and propos...
Optics-Only Calibration of a Neural-Net Based Optical NDE Method for Structural Health Monitoring
Decker, Arthur J.
2004-01-01
A calibration process is presented that uses optical measurements alone to calibrate a neural-net based NDE method. The method itself detects small changes in the vibration mode shapes of structures. The optics-only calibration process confirms previous work that the sensitivity to vibration-amplitude changes can be as small as 10 nanometers. A more practical value in an NDE service laboratory is shown to be 50 nanometers. Both model-generated and experimental calibrations are demonstrated using two implementations of the calibration technique. The implementations are based on previously published demonstrations of the NDE method and an alternative calibration procedure that depends on comparing neural-net and point sensor measurements. The optics-only calibration method, unlike the alternative method, does not require modifications of the structure being tested or the creation of calibration objects. The calibration process can be used to test improvements in the NDE process and to develop a vibration-mode-independence of damagedetection sensitivity. The calibration effort was intended to support NASA s objective to promote safety in the operations of ground test facilities or aviation safety, in general, by allowing the detection of the gradual onset of structural changes and damage.
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.
PETRI NET MODELING OF COMPUTER VIRUS LIFE CYCLE
African Journals Online (AJOL)
Dr Obe
ABSTRACT. Virus life cycle, which refers to the stages of development of a computer virus, is presented as a suitable area for the application of Petri nets. Petri nets a powerful modeling tool in the field of dynamic system analysis is applied to model the virus life cycle. Simulation of the derived model is also presented.
Petri Net Modeling of Computer Virus Life Cycle | Ikekonwu ...
African Journals Online (AJOL)
Virus life cycle, which refers to the stages of development of a computer virus, is presented as a suitable area for the application of Petri nets. Petri nets a powerful modeling tool in the field of dynamic system analysis is applied to model the virus life cycle. Simulation of the derived model is also presented. The intention of ...
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.
Transactions on Petri Nets and Other Models of Concurrency V
DEFF Research Database (Denmark)
submission track of ToPNoC. The 12 papers cover a diverse range of topics including model checking and system verification, synthesis, foundational work on specific classes of Petri nets, and innovative applications of Petri nets and other models of concurrency. Thus, this volume gives a good view of ongoing...
Comparison of the performance of net radiation calculation models
DEFF Research Database (Denmark)
Kjærsgaard, Jeppe Hvelplund; Cuenca, R H; Martinez-Cob, A
2009-01-01
values of net radiation were calculated using three net outgoing long-wave radiation models and compared to measured values. Four meteorological datasets representing two climate regimes, a sub-humid, high-latitude environment and a semi-arid mid-latitude environment, were used to test the models...... or developed for specific climate regimes, the predictions of the physically based model had slightly lower bias and scatter than the empirical models. When used with their original model coefficients, the physically based model had a higher bias than the measurement error of the net radiation instruments used...
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.
Petri Nets as Models of Linear Logic
DEFF Research Database (Denmark)
Engberg, Uffe Henrik; Winskel, Glynn
1990-01-01
The chief purpose of this paper is to appraise the feasibility of Girad's linear logic as a specification language for parallel processes. To this end we propose an interpretation of linear logic in Petri nets, with respect to which we investigate the expressive power of the logic...
Wulsin, D. F.; Gupta, J.R; Mani, R; Blanco, J. A.; Litt, B.
2011-01-01
Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset...
SpaceNet: Modeling and Simulating Space Logistics
Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen
2008-01-01
This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.
Neural-Net Based Optical NDE Method for Structural Health Monitoring
Decker, Arthur J.; Weiland, Kenneth E.
2003-01-01
This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.
Transactions on Petri Nets and Other Models of Concurrency VII
DEFF Research Database (Denmark)
The first section is concerned with the creation of Petri net models and their validation The second section addresses semantic issues and analysis methods The third section is devoted to the automatic synthesis of Petri nets These Transactions publish archival papers in the broad area of Petri n...
Building insightful simulation models using Petri Nets - A structured approach
van der Zee, D.J.
Petri Nets have essential strengths in capturing a system's static structure and dynamics, its mathematical underpinning, and providing a graphical representation. However, visual simulation models of realistic systems based on Petri Nets are often perceived as too large and too complex to be easily
Generalized Net Model of Brevibacterium flavul 22LD Fermentation Process
Directory of Open Access Journals (Sweden)
Olympia Roeva
2005-04-01
Full Text Available In order to render the specific peculiarities of the fermentation processes, as well as to avoid the complexity of mathematical description with systems of differential equations, the elaboration of some new methods and approaches for their modelling and control is predetermined. As a new, alternative approach for modelling of fermentation processes, an application of generalized nets is presented in this paper. The theory of generalized nets is applied to the fermentation process of Brevibacterium flavul 22LD for L-lysine production. A generalized net model of considered process is developed. For comparison and completeness, model with differential equations is also provided. The generalized nets model developed for the fed-batch cultivation of Brevibacterium flavul 22LD allows changing the concentration of the feeding solution and the aeration rate. In this way some inhibition effects are prevented and a possibility for optimal carrying out of the considered fermentation process is provided.
DEFF Research Database (Denmark)
Maurin, Adrian Llopart; Ravn, Ole; Andersen, Nils Axel
2017-01-01
In this paper we present a new method that robustly identifies doors, cabinets and their respective handles, with special emphasis on extracting useful features from handles to be then manipulated. The novelty of this system relies on the combination of a Convolutional Neural Net (CNN), as a form...
Qualifications of Petri nets for modeling of logistical systems
Directory of Open Access Journals (Sweden)
Petr Jedlička
2006-01-01
Full Text Available Simulation and optimization is one of the most popular approaches to improve the use of simulation models as a tool to obtain the best system configuration. Petri nets have proved to be useful for examining the performance of different system configurations and/or alternative operating procedures for various systems. Based on their precise semantics and easy-to-understand graphical representation, Petri nets are appropriate to model logistic systems together with their quantitative and qualitative properties. In this paper, potential benefits of usage of object-oriented Petri nets for optimization of logistical systems are discussed.
Data Sources for NetZero Ft Carson Model
U.S. Environmental Protection Agency — Table of values used to parameterize and evaluate the Ft Carson NetZero integrated Model with published reference sources for each value. This dataset is associated...
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....
Directory of Open Access Journals (Sweden)
A. V. Pavlov
2014-01-01
Full Text Available A mechanism of cognitive dissonance reducing is demonstrated with approach for non-monotonic fuzzy-valued logics by Fourier-holography technique implementation developing. Cognitive dissonance occurs under perceiving of new information that contradicts to the existing subjective pattern of the outside world, represented by double Fourier-transform cascade with a hologram – neural layers interconnections matrix of inner information representation and logical conclusion. The hologram implements monotonic logic according to “General Modus Ponens” rule. New information is represented by a hologram of exclusion that implements interconnections of logical conclusion and exclusion for neural layers. The latter are linked by Fourier transform that determines duality of the algebra forming operations of conjunction and disjunction. Hologram of exclusion forms conclusion that is dual to the “General Modus Ponens” conclusion. It is shown, that trained for the main rule and exclusion system can be represented by two-layered neural network with separate interconnection matrixes for direct and inverse iterations. The network energy function is involved determining the cyclic dynamics character; dissipative factor causing convergence type of the dynamics is analyzed. Both “General Modus Ponens” and exclusion holograms recording conditions on the dynamics and convergence of the system are demonstrated. The system converges to a stable status, in which logical conclusion doesn’t depend on the inner information. Such kind of dynamics, leading to tolerance forming, is typical for ordinary kind of thinking, aimed at inner pattern of outside world stability. For scientific kind of thinking, aimed at adequacy of the inner pattern of the world, a mechanism is needed to stop the net relaxation; the mechanism has to be external relative to the model of logic. Computer simulation results for the learning conditions adequate to real holograms recording are
Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A
2013-08-01
As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.
Directory of Open Access Journals (Sweden)
Zhan Wu
2017-11-01
Full Text Available Facial expression recognition (FER under active near-infrared (NIR illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D, Local binary patterns from three orthogonal planes (LBP-TOP, deep temporal appearance-geometry network (DTAGN, and adapt 3D Convolutional Neural Networks (3D CNN DAP.
Software Design Modelling with Functional Petri Nets | Bakpo ...
African Journals Online (AJOL)
Petri Nets use two basic primitives: events and conditions to view or model a system. Events are the actions that take place in the system. The occurrence of events is controlled by the "state" of the system, which can be described as a set of conditions. An immediate application of such a model is in the control structures of ...
A framework for quantifying net benefits of alternative prognostic models
DEFF Research Database (Denmark)
Rapsomaniki, Eleni; White, Ian R; Wood, Angela M
2012-01-01
New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit......) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk...... reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple...
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...
Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots
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Nicholas Livingston
2016-12-01
Full Text Available While modularity is thought to be central for the evolution of complexity and evolvability, it remains unclear how systems boot-strap themselves into modularity from random or fully integrated starting conditions. Clune et al. (2013 suggested that a positive correlation between sparsity and modularity is the prime cause of this transition. We sought to test the generality of this modularity-sparsity hypothesis by testing it for the first time in physically embodied robots. A population of ten Tadros — autonomous, surface-swimming robots propelled by a flapping tail — was used. Individuals varied only in the structure of their neural net control, a 2 x 6 x 2 network with recurrence in the hidden layer. Each of the 60 possible connections was coded in the genome, and could achieve one of three states: -1, 0, 1. Inputs were two light-dependent resistors and outputs were two motor control variables to the flapping tail, one for the frequency of the flapping and the other for the turning offset. Each Tadro was tested separately in a circular tank lit by a single overhead light source. Fitness was the amount of light gathered by a vertically oriented sensor that was disconnected from the controller net. Reproduction was asexual, with the top performer cloned and then all individuals entered into a roulette wheel selection process, with genomes mutated to create the offspring. The starting population of networks was randomly generated. Over ten generations, the population’s mean fitness increased two-fold. This evolution occurred in spite of an unintentional integer overflow problem in recurrent nodes in the hidden layer that caused outputs to oscillate. Our investigation of the oscillatory behavior showed that the mutual information of inputs and outputs was sufficient for the reactive behaviors observed. While we had predicted that both modularity and sparsity would follow the same trend as fitness, neither did so. Instead, selection gradients
Possibilistic Fuzzy Net Present Value Model and Application
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S. S. Appadoo
2014-01-01
Full Text Available The cash flow values and the interest rate in the net present value (NPV model are usually specified by either crisp numbers or random variables. In this paper, we first discuss some of the recent developments in possibility theory and find closed form expressions for fuzzy possibilistic net present value (FNPV. Then, following Carlsson and Fullér (2001, we discuss some of the possibilistic moments related to FNPV model along with an illustrative numerical example. We also give a unified approach to find higher order moments of FNPV by using the moment generating function introduced by Paseka et al. (2011.
DEFF Research Database (Denmark)
Johansen, Morten Bo; Gonzalez-Izarzugaza, Jose Maria; Brunak, Søren
2013-01-01
We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features...... assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates...... cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further...
Renyi entropies for classical string-net models
Hermanns, M.; Trebst, S.
2014-05-01
In quantum mechanics, string-net condensed states—a family of prototypical states exhibiting nontrivial topological order—can be classified via their long-range entanglement properties, in particular, topological corrections to the prevalent area law of the entanglement entropy. Here we consider classical analogs of such string-net models whose partition function is given by an equal-weight superposition of classical string-net configurations. Our analysis of the Shannon and Renyi entropies for a bipartition of a given system reveals that the prevalent volume law for these classical entropies is augmented by subleading topological corrections that are intimately linked to the anyonic theories underlying the construction of the classical models. We determine the universal values of these topological corrections for a number of underlying anyonic theories including SU(2)k,SU(N)1, and SU(N)2 theories.
Hemmateenejad, Bahram; Ghavami, Raoof; Miri, Ramin; Shamsipur, Majtaba
2006-02-15
Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.
Transactions on Petri Nets and Other Models of Concurrency V
DEFF Research Database (Denmark)
The fifth volume of ToPNoC contains revised versions of selected papers from workshops and tutorials held in conjunction with the 31st International Conference on Application and Theory of Petri Nets and Other Models of Concurrency, as well as a contributed paper selected through the regular subm...
Net Balanced Floorplanning Based on Elastic Energy Model
DEFF Research Database (Denmark)
Liu, Wei; Nannarelli, Alberto
2008-01-01
with balanced net delays to increase the safety margins of the design. In this paper, we investigate the properties of floorplanning based on the elastic energy model. The B*-tree, which is based on an ordered binary tree, is used for circuit representation and the elastic energy is used as the cost function...
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…
Salehi, Seyed Sadegh Mohseni; Erdogmus, Deniz; Gholipour, Ali
2017-06-28
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. State-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry; therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3D image information without the need for computationally expensive 3D convolutions, and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI
Directory of Open Access Journals (Sweden)
Zixuan Cang
2017-07-01
Full Text Available Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH method. ESPH represents 3D complex geometry by one-dimensional (1D topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN. We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes.weilab.math.msu.edu/TDL/.
2017-01-01
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969
Generation of daily solar irradiation by means of artificial neural net works
Energy Technology Data Exchange (ETDEWEB)
Siqueira, Adalberto N.; Tiba, Chigueru; Fraidenraich, Naum [Departamento de Energia Nuclear, da Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, 1000 - CDU, CEP 50.740-540 Recife, Pernambuco (Brazil)
2010-11-15
The present study proposes the utilization of Artificial Neural Networks (ANN) as an alternative for generating synthetic series of daily solar irradiation. The sequences were generated from the use of daily temporal series of a group of meteorological variables that were measured simultaneously. The data used were measured between the years of 1998 and 2006 in two temperate climate localities of Brazil, Ilha Solteira (Sao Paulo) and Pelotas (Rio Grande do Sul). The estimates were taken for the months of January, April, July and October, through two models which are distinguished regarding the use or nonuse of measured bright sunshine hours as an input variable. An evaluation of the performance of the 56 months of solar irradiation generated by way of ANN showed that by using the measured bright sunshine hours as an input variable (model 1), the RMSE obtained were less or equal to 23.2% being that of those, although 43 of those months presented RMSE less or equal to 12.3%. In the case of the model that did not use the measured bright sunshine hours but used a daylight length (model 2), RMSE were obtained that varied from 8.5% to 37.5%, although 38 of those months presented RMSE less or equal to 20.0%. A comparison of the monthly series for all of the years, achieved by means of the Kolmogorov-Smirnov test (to a confidence level of 99%), demonstrated that of the 16 series generated by ANN model only two, obtained by model 2 for the months of April and July in Pelotas, presented significant difference in relation to the distributions of the measured series and that all mean deviations obtained were inferior to 0.39 MJ/m{sup 2}. It was also verified that the two ANN models were able to reproduce the principal statistical characteristics of the frequency distributions of the measured series such as: mean, mode, asymmetry and Kurtosis. (author)
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.
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.
AN-CASE NET-CENTRIC modeling and simulation
Baskinger, Patricia J.; Chruscicki, Mary Carol; Turck, Kurt
2009-05-01
The objective of mission training exercises is to immerse the trainees into an environment that enables them to train like they would fight. The integration of modeling and simulation environments that can seamlessly leverage Live systems, and Virtual or Constructive models (LVC) as they are available offers a flexible and cost effective solution to extending the "war-gaming" environment to a realistic mission experience while evolving the development of the net-centric enterprise. From concept to full production, the impact of new capabilities on the infrastructure and concept of operations, can be assessed in the context of the enterprise, while also exposing them to the warfighter. Training is extended to tomorrow's tools, processes, and Tactics, Techniques and Procedures (TTPs). This paper addresses the challenges of a net-centric modeling and simulation environment that is capable of representing a net-centric enterprise. An overview of the Air Force Research Laboratory's (AFRL) Airborne Networking Component Architecture Simulation Environment (AN-CASE) is provide as well as a discussion on how it is being used to assess technologies for the purpose of experimenting with new infrastructure mechanisms that enhance the scalability and reliability of the distributed mission operations environment.
Modelling the multiple sclerosis disease using stochastic petri nets
Μικελλίδης, Λοΐζος; Mikellides, Loizos
2011-01-01
Multiple Sclerosis is an inflammatory demyelinating disorder of the Central Nervous System. It is an autoimmune disease that its causes are still not clarified. The demyelization of the neural axons leads to the physical and cognitive disability of the patient. It is important to model the pathophysiology of Multiple Sclerosis in order to obtain an insight of the problem. Formal methods help in this direction by providing concepts and disciplines that are applicable to biological systems, ...
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.
Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks
Directory of Open Access Journals (Sweden)
Jian Jin
2015-01-01
Full Text Available When pure linear neural network (PLNN is used to predict tropical cyclone tracks (TCTs in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1 and (0, 1; with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1 and (100, 1. We present the following results: (1 data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2 mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.
Competition and Cooperation in Neural Nets : U.S.-Japan Joint Seminar
Arbib, Michael
1982-01-01
The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers ...
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.
Petri net modeling of encrypted information flow in federated cloud
Khushk, Abdul Rauf; Li, Xiaozhong
2017-08-01
Solutions proposed and developed for the cost-effective cloud systems suffer from a combination of secure private clouds and less secure public clouds. Need to locate applications within different clouds poses a security risk to the information flow of the entire system. This study addresses this by assigning security levels of a given lattice to the entities of a federated cloud system. A dynamic flow sensitive security model featuring Bell-LaPadula procedures is explored that tracks and authenticates the secure information flow in federated clouds. Additionally, a Petri net model is considered as a case study to represent the proposed system and further validate the performance of the said system.
Net-Zero-Energy Model for Sustainable Wastewater Treatment.
Yan, Peng; Qin, Rong-Cong; Guo, Jin-Song; Yu, Qiang; Li, Zhe; Chen, You-Peng; Shen, Yu; Fang, Fang
2017-01-17
A large external energy input prevents wastewater treatment from being environmentally sustainable. A net-zero-energy (NZE) wastewater treatment concept based on biomass energy recycling was proposed to avoid wasting resources and to promote energy recycling in wastewater treatment plants (WWTPs). Simultaneously, a theoretical model and boundary condition based on energy balance were established to evaluate the feasibility of achieving NZE in WWTPs; the model and condition were employed to analyze data from 20 conventional WWTPs in China. A total of six WWTPs can currently export excess energy, eight WWTPs can achieve 100% energy self-sufficiency by adjusting the metabolic material allocation, and six municipal WWTPs cannot achieve net-zero energy consumption based on the evaluation of the theoretical model. The NZE model offset 79.5% of the electricity and sludge disposal cost compared with conventional wastewater treatment. The NZE model provides a theoretical basis for the optimization of material regulation for the effective utilization of organic energy from wastewater and promotes engineering applications of the NZE concept in WWTPs.
A Petri Net model for distributed energy system
Konopko, Joanna
2015-12-01
Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of the model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.
Macro-modelling via radial basis functionen nets
Wiegand, C.; Fischer, C.; Kazemzadeh, R.; Hedayat, C.; John, W.; Hilleringmann, U.
2008-05-01
By the rising complexity and miniaturisation of the device's dimensions, the density of the conductors increases considerably. Referring to this, locally transient interactions between single physical values become apparent. Therefore, for the investigation and optimisation of integrated circuits it is essential to develop suitable models and simulation surroundings which allow for memory and time-efficient calculation of the behaviour. By means of the dynamic reconstruction theory and the radial basis functions nets the so-called black box models are provided. The description of black box models is derived from the input and output behaviour or so-called time series of a dynamic system. Concerning the time series, the black box model adapts its parameters via the extended Kalman filter. This paper provides a modelling approach that enables fast and efficient simulations.
Macro-modelling via radial basis functionen nets
Directory of Open Access Journals (Sweden)
C. Wiegand
2008-05-01
Full Text Available By the rising complexity and miniaturisation of the device's dimensions, the density of the conductors increases considerably. Referring to this, locally transient interactions between single physical values become apparent. Therefore, for the investigation and optimisation of integrated circuits it is essential to develop suitable models and simulation surroundings which allow for memory and time-efficient calculation of the behaviour. By means of the dynamic reconstruction theory and the radial basis functions nets the so-called black box models are provided. The description of black box models is derived from the input and output behaviour or so-called time series of a dynamic system. Concerning the time series, the black box model adapts its parameters via the extended Kalman filter. This paper provides a modelling approach that enables fast and efficient simulations.
A Petri Net model for distributed energy system
Energy Technology Data Exchange (ETDEWEB)
Konopko, Joanna [Warsaw University of Technology, The Faculty of Electronics and Information Technology, Institute of Computer Science, Nowowiejska Street 15/19, Warsaw 00-665 (Poland)
2015-12-31
Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of the model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.
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.
Detection and Modeling of Cyber Attacks with Petri Nets
Directory of Open Access Journals (Sweden)
Bartosz Jasiul
2014-12-01
Full Text Available The aim of this article is to present an approach to develop and verify a method of formal modeling of cyber threats directed at computer systems. Moreover, the goal is to prove that the method enables one to create models resembling the behavior of malware that support the detection process of selected cyber attacks and facilitate the application of countermeasures. The most common cyber threats targeting end users and terminals are caused by malicious software, called malware. The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models. As the obfuscation techniques make the malware almost undetectable, the classic signature-based anti-virus tools must be supported with behavioral analysis. The proposed approach to modeling of malware behavior is based on colored Petri nets. This article is addressed to cyber defense researchers, security architects and developers solving up-to-date problems regarding the detection and prevention of advanced persistent threats.
Formal Requirements Modeling for Reactive Systems with Coloured Petri Nets
DEFF Research Database (Denmark)
Tjell, Simon
This dissertation presents the contributions of seven publications all concerned with the application of Coloured Petri Nets (CPN) to requirements modeling for reactive systems. The publications are introduced along with relevant background material and related work, and their contributions...... to addressing the problem of validating formal requirements models through interactive graphical animations is presented. Executable Use Cases (EUCs) provide a framework for integrating three tiers of descriptions of specifications and environment assumptions: the lower tier is an informal description...... activity. The traces are automatically recorded during execution of the model. The second publication presents a formally specified framework for automating a large part of the tasks related to integrating Problem Frames with CPN. The framework is specified in VDM++, and allows the modeler to automatically...
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...
Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David
1998-01-01
The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.
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 ...
Multilevel models for estimating incremental net benefits in multinational studies.
Grieve, Richard; Nixon, Richard; Thompson, Simon G; Cairns, John
2007-08-01
Multilevel models (MLMs) have been recommended for estimating incremental net benefits (INBs) in multicentre cost-effectiveness analysis (CEA). However, these models have assumed that the INBs are exchangeable and that there is a common variance across all centres. This paper examines the plausibility of these assumptions by comparing various MLMs for estimating the mean INB in a multinational CEA. The results showed that the MLMs that assumed the INBs were exchangeable and had a common variance led to incorrect inferences. The MLMs that included covariates to allow for systematic differences across the centres, and estimated different variances in each centre, made more plausible assumptions, fitted the data better and led to more appropriate inferences. We conclude that the validity of assumptions underlying MLMs used in CEA need to be critically evaluated before reliable conclusions can be drawn. Copyright 2006 John Wiley & Sons, Ltd.
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...
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.
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.
Fast neural-net based fake track rejection in the LHCb reconstruction
De Cian, Michel; Seyfert, Paul; Stahl, Sascha
2017-01-01
A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, retains more than 99 % of well reconstructed tracks while reducing the number of fake tracks by 60 %. It is fast enough to fit into the CPU time budget of the software trigger farm and thus reduces the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment in Run II of the LHC.
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
Modelling safety of gantry crane operations using Petri nets.
Singh, Karmveer; Raj, Navneet; Sahu, S K; Behera, R K; Sarkar, Sobhan; Maiti, J
2017-03-01
Being a powerful tool in modelling industrial and service operations, Petri net (PN) has been extremely used in different domains, but its application in safety study is limited. In this study, we model the gantry crane operations used for industrial activities using generalized stochastic PNs. The complete cycle of operations of the gantry crane is split into three parts namely inspection and loading, movement of load, and unloading of load. PN models are developed for all three parts and the whole system as well. The developed PN models have captured the safety issues through reachability tree. The hazardous states are identified and how they ultimately lead to some unwanted accidents is demonstrated. The possibility of falling of load and failure of hook, sling, attachment and hoist rope are identified. Possible suggestions based on the study are presented for redesign of the system. For example, mechanical stoppage of operations in case of loosely connected load, and warning system for use of wrong buttons is tested using modified models.
Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP
Directory of Open Access Journals (Sweden)
Stanimir Surchev
2013-10-01
Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.
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].
PETRI NET MODELING OF COMPUTER VIRUS LIFE CYCLE
African Journals Online (AJOL)
Dr Obe
Virus life cycle, which refers to the stages of development of a computer virus, is presented as a suitable area for the application of Petri ... used to derive anti-viral programs based on the Petri net framework. Keywords: Virus lifecycle, Petri nets, ... are often disguised as images, joke software or anti-virus software. We have ...
Mehta, Raghav; Majumdar, Aabhas; Sivaswamy, Jayanthi
2017-04-01
Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.
HAWC Analysis of the Crab Nebula Using Neural-Net Energy Reconstruction
Marinelli, Samuel; HAWC Collaboration
2017-01-01
The HAWC (High-Altitude Water-Cherenkov) experiment is a TeV γ-ray observatory located 4100 m above sea level on the Sierra Negra mountain in Puebla, Mexico. The detector consists of 300 water-filled tanks, each instrumented with 4 photomuliplier tubes that utilize the water-Cherenkov technique to detect atmospheric air showers produced by cosmic γ rays. Construction of HAWC was completed in March, 2015. The experiment's wide field of view (2 sr) and high duty cycle (> 95 %) make it a powerful survey instrument sensitive to pulsar wind nebulae, supernova remnants, active galactic nuclei, and other γ-ray sources. The mechanisms of particle acceleration at these sources can be studied by analyzing their energy spectra. To this end, we have developed an event-by-event energy-reconstruction algorithm employing an artificial neural network to estimate energies of primary γ rays. The Crab Nebula, the brightest source of TeV photons, makes an excellent calibration source for this technique. We will present preliminary results from an analysis of the Crab energy spectrum using this new energy-reconstruction method. This work was supported by the National Science Foundation.
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.
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.
Petri Net Modeling and Decomposition Method for Solving Production Scheduling Problems
National Research Council Canada - National Science Library
NISHI, Tatsushi; MAENO, Ryota
2007-01-01
Considering the need to develop general scheduling problem solver, the recent integration of Petri Nets as modeling tools into effective optimization methods for scheduling problems is very promising...
No-net-rotation model of current plate velocities incorporating plate motion model NUVEL-1
Argus, Donald F.; Gordon, Richard G.
1991-01-01
NNR-NUVEL1 is presented which is a model of plate velocities relative to the unique reference frame defined by requiring no-net-rotation of the lithosphere while constraining relative plate velocities to equal those in global plate motion model NUVEL-1 (DeMets et al., 1990). In NNR-NUVEL1, the Pacific plate rotates in a right-handed sense relative to the no-net-rotation reference frame at 0.67 deg/m.y. about 63 deg S, 107 deg E. At Hawaii the Pacific plate moves relative to the no-net-rotation reference frame at 70 mm/yr, which is 25 mm/yr slower than the Pacific plate moves relative to the hotspots. Differences between NNR-NUVEL1 and HS2-NUVEL1 are described. The no-net-rotation reference frame differs significantly from the hotspot reference frame. If the difference between reference frames is caused by motion of the hotspots relative to a mean-mantle reference frame, then hotspots beneath the Pacific plate move with coherent motion towards the east-southeast. Alternatively, the difference between reference frames can show that the uniform drag, no-net-torque reference frame, which is kinematically equivalent to the no-net-rotation reference frame, is based on a dynamically incorrect premise.
No-net-rotation model of current plate velocities incorporating plate motion model NUVEL-1
Argus, Donald F.; Gordon, Richard G.
1991-11-01
NNR-NUVEL1 is presented which is a model of plate velocities relative to the unique reference frame defined by requiring no-net-rotation of the lithosphere while constraining relative plate velocities to equal those in global plate motion model NUVEL-1 (DeMets et al., 1990). In NNR-NUVEL1, the Pacific plate rotates in a right-handed sense relative to the no-net-rotation reference frame at 0.67 deg/m.y. about 63 deg S, 107 deg E. At Hawaii the Pacific plate moves relative to the no-net-rotation reference frame at 70 mm/yr, which is 25 mm/yr slower than the Pacific plate moves relative to the hotspots. Differences between NNR-NUVEL1 and HS2-NUVEL1 are described. The no-net-rotation reference frame differs significantly from the hotspot reference frame. If the difference between reference frames is caused by motion of the hotspots relative to a mean-mantle reference frame, then hotspots beneath the Pacific plate move with coherent motion towards the east-southeast. Alternatively, the difference between reference frames can show that the uniform drag, no-net-torque reference frame, which is kinematically equivalent to the no-net-rotation reference frame, is based on a dynamically incorrect premise.
van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved
2017-01-01
NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.
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.
Energy Technology Data Exchange (ETDEWEB)
Kertzman, M.P. (Department of Physics and Astronomy, DePauw University Greencastle, Indiana 46135 (USA)); Sembroski, G.H. (Department of Physcis, Purdue University West Lafayette, Indiana 47907 (USA))
1991-04-05
Detailed 3-dimensional Monte-Carlo computer simulations of the Cherenkov photons produced by VHE (10 GeV to 10 TeV) gamma ray and proton induced air shower cascades are used to calculate the sensitivity and threshold of a ground-based, single-mount, multi-mirror, single photo-electron sensitive gamma ray telescope. Such a telescope is designed to have the lowest possible energy threshold for gamma ray induced air showers for a given light collection area. The sensitivity and energy threshold of this design are determined for various triggering configurations, and the sources and properties of background triggers are investigated. In particular, it is found that up to 40% of the background triggers are due to single muons produced by proton induced showers with primary energies in the 25 to 75 GeV range. Two methods for increasing the sensitivity of such a telescope by discrimination against the single muon induced triggers are investigated. The first uses small outrider telescopes triggering in coincidence with the main telescope. The second uses software implemented neural nets trained to identify muon induced triggers by use of the temporal shape of the Cherenkov light pulse.
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.
Testing Benjamin Graham’s net current asset value model
Directory of Open Access Journals (Sweden)
Chongsoo An
2015-02-01
Full Text Available The objective of this paper is to empirically test one of Graham’s investment methods based on the net current asset value (NCAV. The NCAV is truly unique, and conservative, and commonly known as the net-net method. The ratio of the net current asset value to market value (NCAV/MV was employed in this study to test a stock’s performance comparing to the performance of S&P 500 as the market index. We used all stocks in Portfolio123 whose raw data were supplied by Compustat, Standard & Poors, Capital IQ, and Reuters for the period of January 2, 1999 to August 31, 2012. The overall results show that the firms with high net current asset values outperform the market. These results are strong in the up market. It can be argued that the firms with a high NCAV/MV ratio are likely to move toward their fundamental value and generate high excess return because its stock prices are now undervalued. The implications of the study are: (a a positive NCAV/MV ratio may be a good indicator of the underpriced security; (b investing in the growth period and avoiding the downturn period leads investors to earn much higher returns from the firms with a high NCAV/MV ratio; and (c The NCAV/MV strategy requires a longer holding period of the portfolio in order to generate excess returns.
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....
BioModels.net Web Services, a free and integrated toolkit for computational modelling software.
Li, Chen; Courtot, Mélanie; Le Novère, Nicolas; Laibe, Camille
2010-05-01
Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.
NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment
Chirayath, Ved
2017-01-01
In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration
Transactions on Petri Nets and Other Models of Concurrency VI
DEFF Research Database (Denmark)
The sixth volume of ToPNoC includes revised versions of selected papers from workshops and tutorials held at the 32nd International Conference on Application and Theory of Petri Nets and Concurrency. It also contains a special section on Networks, Protocols, and Services, as well as a contributed...
Estimating net present value variability for deterministic models
van Groenendaal, W.J.H.
1995-01-01
For decision makers the variability in the net present value (NPV) of an investment project is an indication of the project's risk. So-called risk analysis is one way to estimate this variability. However, risk analysis requires knowledge about the stochastic character of the inputs. For large,
Application of growing nested Petri nets for modeling robotic systems operating under risk
Sorokin, E. V.; Senkov, A. V.
2017-10-01
The paper studies the peculiarities of modeling robotic systems engaged in mining. Existing modeling mechanisms are considered, which are based on nested Petri nets, and a new formalism of growing Petri nets is presented that allows modeling robotic systems operating under risk. Modeling is provided both for the regular operation mode and for non-standard modes in which individual elements of the system can perform uncharacteristic functions. The example shows growing Petri nets that are used for modeling extraction of flat coal seams by a robotic system consisting of several different-type autonomous robots.
Coloured Petri Nets and CPN Tools for Modelling and Validation of Concurrent Systems
DEFF Research Database (Denmark)
Jensen, Kurt; Kristensen, Lars Michael; Wells, Lisa Marie
2007-01-01
Coloured Petri Nets (CPNs) is a language for the modeling and validation og systems in which concurrency, communication, and synchronisation play a major role. Coloured Petri Nets is a descrete-event modeling language combining Petri Nets with the funcitonal programming language Standard ML. Petri...... taken to execute events in the modelled system. CPN Tolls is an industrial-strength computer tool for construction and analysing CPN models. Using CPN Tools, it is possible to investigate the behaviour of the modelled system using simulation, to verify properties by means of state sp0ece methods...
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.
Computational Modeling aided Near Net Shape Manufacturing for Aluminum Alloys Project
National Aeronautics and Space Administration — This program will focus on developing and validating computational models for near-net shape processing of aluminum alloys. Computational models will be developed...
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 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
Modeling biological gradient formation: combining partial differential equations and Petri nets.
Bertens, Laura M F; Kleijn, Jetty; Hille, Sander C; Heiner, Monika; Koutny, Maciej; Verbeek, Fons J
2016-01-01
Both Petri nets and differential equations are important modeling tools for biological processes. In this paper we demonstrate how these two modeling techniques can be combined to describe biological gradient formation. Parameters derived from partial differential equation describing the process of gradient formation are incorporated in an abstract Petri net model. The quantitative aspects of the resulting model are validated through a case study of gradient formation in the fruit fly.
Agent-Based Modelling: Tools for Linking NetLogo and r
Jan C Thiele; Winfried Kurth; Volker Grimm
2012-01-01
A seamless integration of software platforms for implementing agent-based models and for analysing their output would facilitate comprehensive model analyses and thereby make agent-based modelling more useful. Here we report on recently developed tools for linking two widely used software platforms: NetLogo for implementing agent-based models, and R for the statistical analysis and design of experiments. Embedding R into NetLogo allows the use of advanced statistical analyses, specific statis...
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....
Wulsin, D. F.; Gupta, J. R.; Mani, R.; Blanco, J. A.; Litt, B.
2011-06-01
Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data—a rarity in automated physiological waveform analysis—with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.
Jofrehei, Arash
2015-01-01
Current track reconstruction methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast. Simulation might not be as realistic as real data but tracking efficiency is 100 percent for that while by using real data we would probably be limited to current efficiency. The fact that this approach can be a lot faster and even more efficient than current methods by using simulation data can make it a great alternative for current track reconstruction methods used in both triggering and tracking.
[A computer model of "sense of humor". II. Realization in neuronal nets].
Suslov, I M
1992-01-01
The Hopfield model of neural networks can be adapted for solving the "linguistic problems", i.e. the processing of a continuous succession of polysemantic images. The realization of "a sense of humor" in such a model is possible.
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...
The CAFE model: A net production model for global ocean phytoplankton
Silsbe, Greg M.; Behrenfeld, Michael J.; Halsey, Kimberly H.; Milligan, Allen J.; Westberry, Toby K.
2016-12-01
The Carbon, Absorption, and Fluorescence Euphotic-resolving (CAFE) net primary production model is an adaptable framework for advancing global ocean productivity assessments by exploiting state-of-the-art satellite ocean color analyses and addressing key physiological and ecological attributes of phytoplankton. Here we present the first implementation of the CAFE model that incorporates inherent optical properties derived from ocean color measurements into a mechanistic and accurate model of phytoplankton growth rates (μ) and net phytoplankton production (NPP). The CAFE model calculates NPP as the product of energy absorption (QPAR), and the efficiency (ϕμ) by which absorbed energy is converted into carbon biomass (CPhyto), while μ is calculated as NPP normalized to CPhyto. The CAFE model performance is evaluated alongside 21 other NPP models against a spatially robust and globally representative set of direct NPP measurements. This analysis demonstrates that the CAFE model explains the greatest amount of variance and has the lowest model bias relative to other NPP models analyzed with this data set. Global oceanic NPP from the CAFE model (52 Pg C m-2 yr-1) and mean division rates (0.34 day-1) are derived from climatological satellite data (2002-2014). This manuscript discusses and validates individual CAFE model parameters (e.g., QPAR and ϕμ), provides detailed sensitivity analyses, and compares the CAFE model results and parameterization to other widely cited models.
Lin Chen; Linxiang Shi; Wen'an Tan
2012-01-01
Modeling and performance evaluation plays an important role on the theoretical research and technology improvement of the Internet of Things (IoT). In the study, the modeling and performance evaluation method based on Petri Nets and behavior expression is presented. Firstly, according to the system information flow chart, the constraint relationship between places and transitions are identified and then graphic Extended Stochastic Petri Nets model is built up; next, the behavior expression me...
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.
A Petri Net-Based Software Process Model for Developing Process-Oriented Information Systems
Li, Yu; Oberweis, Andreas
Aiming at increasing flexibility, efficiency, effectiveness, and transparency of information processing and resource deployment in organizations to ensure customer satisfaction and high quality of products and services, process-oriented information systems (POIS) represent a promising realization form of computerized business information systems. Due to the complexity of POIS, explicit and specialized software process models are required to guide POIS development. In this chapter we characterize POIS with an architecture framework and present a Petri net-based software process model tailored for POIS development with consideration of organizational roles. As integrated parts of the software process model, we also introduce XML nets, a variant of high-level Petri nets as basic methodology for business processes modeling, and an XML net-based software toolset providing comprehensive functionalities for POIS development.
AirMOSS: L4 Modeled Net Ecosystem Exchange (NEE), Continental USA, 2012-2014
National Aeronautics and Space Administration — This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are...
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.
Krasilenko, Vladimir G.; Lazarev, Alexander A.; Grabovlyak, Sveta K.; Nikitovich, Diana V.
2013-01-01
We consider equivalency models, including matrix-matrix and matrix-tensor and with the dual adaptive-weighted correlation, multi-port neural-net auto-associative and hetero-associative memory (MP NN AAM and HAP), which are equivalency paradigm and the theoretical basis of our work. We make a brief overview of the possible implementations of the MP NN AAM and of their architectures proposed and investigated earlier by us. The main base unit of such architectures is a matrix-matrix or matrix-tensor equivalentor. We show that the MP NN AAM based on the equivalency paradigm and optoelectronic architectures with space-time integration and parallel-serial 2D images processing have advantages such as increased memory capacity (more than ten times of the number of neurons!), high performance in different modes (1010 - 1012 connections per second!) And the ability to process, store and associatively recognize highly correlated images. Next, we show that with minor modifications, such MP NN AAM can be successfully used for highperformance parallel clustering processing of images. We show simulation results of using these modifications for clustering and learning models and algorithms for cluster analysis of specific images and divide them into categories of the array. Show example of a cluster division of 32 images (40x32 pixels) letters and graphics for 12 clusters with simultaneous formation of the output-weighted space allocated images for each cluster. We discuss algorithms for learning and self-learning in such structures and their comparative evaluations based on Mathcad simulations are made. It is shown that, unlike the traditional Kohonen self-organizing maps, time of learning in the proposed structures of multi-port neuronet classifier/clusterizer (MP NN C) on the basis of equivalency paradigm, due to their multi-port, decreases by orders and can be, in some cases, just a few epochs. Estimates show that in the test clustering of 32 1280- element images into 12
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.
An assembly process model based on object-oriented hierarchical time Petri Nets
Wang, Jiapeng; Liu, Shaoli; Liu, Jianhua; Du, Zenghui
2017-04-01
In order to improve the versatility, accuracy and integrity of the assembly process model of complex products, an assembly process model based on object-oriented hierarchical time Petri Nets is presented. A complete assembly process information model including assembly resources, assembly inspection, time, structure and flexible parts is established, and this model describes the static and dynamic data involved in the assembly process. Through the analysis of three-dimensional assembly process information, the assembly information is hierarchically divided from the whole, the local to the details and the subnet model of different levels of object-oriented Petri Nets is established. The communication problem between Petri subnets is solved by using message database, and it reduces the complexity of system modeling effectively. Finally, the modeling process is presented, and a five layer Petri Nets model is established based on the hoisting process of the engine compartment of a wheeled armored vehicle.
Directory of Open Access Journals (Sweden)
Eric Bastos Gorgens
2009-12-01
Full Text Available Rede neural artificial consiste em um conjunto de unidades que contêm funções matemáticas, unidas por pesos. As redes são capazes de aprender, mediante modificação dos pesos sinápticos, e generalizar o aprendizado para outros arquivos desconhecidos. O projeto de redes neurais é composto por três etapas: pré-processamento, processamento e, por fim, pós-processamento dos dados. Um dos problemas clássicos que podem ser abordados por redes é a aproximação de funções. Nesse grupo, pode-se incluir a estimação do volume de árvores. Foram utilizados quatro arquiteturas diferentes, cinco pré-processamentos e duas funções de ativação. As redes que se apresentaram estatisticamente iguais aos dados observados também foram analisadas quanto ao resíduo e à distribuição dos volumes e comparadas com a estimação de volume pelo modelo de Schumacher e Hall. As redes neurais formadas por neurônios, cuja função de ativação era exponencial, apresentaram estimativas estatisticamente iguais aos dados observados. As redes treinadas com os dados normalizados pelo método da interpolação linear e equalizados tiveram melhor desempenho na estimação.The artificial neural network consists of a set of units containing mathematical functions connected by weights. Such nets are capable of learning by means of synaptic weight modification, generalizing learning for other unknown archives. The neural network project comprises three stages: pre-processing, processing and post-processing of data. One of the classical problems approached by networks is function approximation. Tree volume estimate can be included in this group. Four different architectures, five pre-processings and two activation functions were used. The nets which were statistically similar to the observed data were also analyzed in relation to residue and volume and compared to the volume estimate provided by the Schumacher and Hall equation. The neural nets formed by
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Elston Timothy C
2004-03-01
Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.
Distinguishing Environment and System in Coloured Petri Net Models of Reactive Systems
DEFF Research Database (Denmark)
Tjell, Simon
2007-01-01
code-generation from models. A prototypical tool has been implemented for performing the structural analysis of Coloured Petri Net models and the principles of this tool is described. The aim of the paper is to make the guidelines and their formalized definitions along with a proof......This paper introduces and formally defines the environment-and-system-partitioned property for behavioral models of reactive systems expressed in the formal modeling language Coloured Petri Net. The purpose of the formalization is to make it possible to automatically validate any CPN model...
Simulation of a Petri net-based model of the terpenoid biosynthesis pathway.
Hawari, Aliah Hazmah; Mohamed-Hussein, Zeti-Azura
2010-02-09
The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN). The biological data needed to construct the terpenoid metabolic model were gathered from the literature and from biological databases. These data were used as building blocks to create an HFPNe model and to generate parameters that govern the global behaviour of the model. The dynamic model was simulated and validated against known experimental data obtained from extensive literature searches. The model successfully simulated metabolite concentration changes over time (pt) and the observations correlated with known data. Interactions between the intermediates that affect the production of terpenes could be observed through the introduction of inhibitors that established feedback loops within and crosstalk between the pathways. Although this metabolic model is only preliminary, it will provide a platform for analysing various high-throughput data, and it should lead to a more holistic understanding of terpenoid biosynthesis.
RNetLogo: an R package for running and exploring individual‐based models implemented in NetLogo
National Research Council Canada - National Science Library
Thiele, Jan C; Kurth, Winfried; Grimm, Volker
2012-01-01
1. NetLogo is a free software platform for implementing individual‐based and agent‐based models. However, NetLogo ’s support of systematic design, performance and analysis of simulation experiments is limited...
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...
NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data
Liu, Jinxun; Chen, J.M.; Price, D.T.; Liu, S.
2005-01-01
NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland C + + Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL C + +, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoint for showing NetCDF map animations are given.
Directory of Open Access Journals (Sweden)
Felix Rembold
2013-03-01
Full Text Available For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI. Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+ images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which
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.
Romano, Renan A.; Pratavieira, Sebastião.; da Silva, Ana P.; Kurachi, Cristina; Guimarães, Francisco E. G.
2017-07-01
This study clearly demonstrates that multispectral confocal microscopy images analyzed by artificial neural networks provides a powerful tool to real-time monitoring photosensitizer uptake, as well as photochemical transformations occurred.
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.
Interval Abstraction Refinement for Model Checking of Timed-Arc Petri Nets
DEFF Research Database (Denmark)
Viesmose, Sine Lyhne; Jacobsen, Thomas Stig; Jensen, Jacob Jon
2014-01-01
-space exploration techniques. We suggest an approximation method for reducing the size of constants present in the model. The proposed method is developed for Timed-Arc Petri Nets and creates an under-approximation or an over-approximation of the model behaviour. The verification of approximated Petri net models...... can be considerably faster but it does not in general guarantee conclusive answers. We implement the algorithms within the open-source model checker TAPAAL and demonstrate on a number of experiments that our approximation techniques often result in a significant speed-up of the verification....
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
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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.
DEFF Research Database (Denmark)
Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl
2010-01-01
is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino......β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method...... NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which...
Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets
Petrosyan, G.; Ter-Vardanyan, L.; Gaboutchian, A.
2017-05-01
Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.
XML views. Part II. Modeling conceptual views using XSemantic nets
Rajugan, R.; Chang, E.; Feng, L.; Dillon, T.
2004-01-01
For complex industrial informatics applications, there exists a requirement to model and design views at a higher level of abstraction. Since the classical view definitions are only available at the data, query or instance level, modelling and maintaining views for such complex systems is an
Directory of Open Access Journals (Sweden)
Yi-Chung Hu
2017-10-01
Full Text Available Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1 model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1 model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem—foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.
ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR
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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.
Hemmen, J; Schulten, Klaus
1994-01-01
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...
METHOD OF DISPLAYING AN EXECUTABLE BUSINESS PROCESS MODELS INTO PETRI NETS
Directory of Open Access Journals (Sweden)
Igor G. Fedorov
2013-01-01
Full Text Available Executable business process models, as well as programs, require evidence of a defect-free finish. The methods based on the formalism of Petri nets are widely used. A business process is a network of dishes, and its properties are set by the analysis of the properties of the network. The aim is to study the methods of displaying an executable business process model in a Petri net. Analysis of the properties of the resulting model allows us to prove a number of important properties: it is a network of free choice and clean without looping.
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.
Modelling Local and Global Behaviour: Petri Nets and Event Coordination
DEFF Research Database (Denmark)
Kindler, Ekkart
2012-01-01
call it the Event Coordination Notation (ECNO). ECNO’s coordination diagrams define the global behaviour of a system only: they define how the local behaviour is coordinated and jointly executed in so-called interactions. In principle, ECNO is independent from a specific notation for modelling......Today, it is possible to generate major parts of a software system from models. Most of the generated code, however, concerns the structural parts of the software; the code that concerns the actual functionality or behaviour of the software system is often still programmed manually. In order...... with existing structural models, with existing code, and with other behavioural models. Basically, coordination diagrams define how the local behaviour of the individual parts of the software is coordinated with each other. The main concepts of coordination diagrams and their semantics are stabilising now: We...
Directory of Open Access Journals (Sweden)
Bent Petersen
Full Text Available UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.
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 ...
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.
Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike
2017-11-01
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.
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.
The Knowledge Building Paradigm: A Model of Learning for Net Generation Students
Philip, Donald
2005-01-01
In this article Donald Philip describes Knowledge Building, a pedagogy based on the way research organizations function. The global economy, Philip argues, is driving a shift from older, industrial models to the model of the business as a learning organization. The cognitive patterns of today's Net Generation students, formed by lifetime exposure…
Extension of Petri Nets by Aspects to Apply the Model Driven Architecture Approach
Roubtsova, E.E.; Aksit, Mehmet
2005-01-01
Within MDA models are usually created in the UML. However, one may prefer to use different notations such as Petri-nets, for example, for modelling concurrency and synchronization properties of systems. This paper claims that techniques that are adopted within the context of MDA can also be
Guo, Jing; Lin, Feng; Zhang, Xiaomeng; Tanavde, Vivek; Zheng, Jie
2017-05-15
Waddington's epigenetic landscape is a powerful metaphor for cellular dynamics driven by gene regulatory networks (GRNs). Its quantitative modeling and visualization, however, remains a challenge, especially when there are more than two genes in the network. A software tool for Waddington's landscape has not been available in the literature. We present NetLand, an open-source software tool for modeling and simulating the kinetic dynamics of GRNs, and visualizing the corresponding Waddington's epigenetic landscape in three dimensions without restriction on the number of genes in a GRN. With an interactive and graphical user interface, NetLand can facilitate the knowledge discovery and experimental design in the study of cell fate regulation (e.g. stem cell differentiation and reprogramming). NetLand can run under operating systems including Windows, Linux and OS X. The executive files and source code of NetLand as well as a user manual, example models etc. can be downloaded from http://netland-ntu.github.io/NetLand/ . zhengjie@ntu.edu.sg. Supplementary data are available at Bioinformatics online.
Chang, C.-H.; Johnson, N. C.; Cassar, N.
2014-06-01
Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of one to two weeks and with six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This nonparametric approach is based entirely on the observed statistical relationships between NCP and the predictors and, therefore, is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published, independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the Subtropical Front in the Atlantic, Indian, and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, the Patagonian Shelf, the Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 17.9 mmol C m-2 d-1, falls within the range of 8.3 to 24 mmol
Chang, C.-H.; Johnson, N. C.; Cassar, N.
2013-10-01
Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of 1-2 weeks, and six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This non-parametric approach is based entirely on the observed statistical relationships between NCP and the predictors, and therefore is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the subtropical front in the Atlantic, Indian and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, Patagonian Shelf, Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 14 mmol C m-2 d-1, falls within the range of 8.3-24 mmol C m-2 d-1 from other model
A model of airport security work flow based on petri net
Dong, Xinming
2017-09-01
Extremely long lines at airports in the United States have been sharply criticized. In order to find out the bottleneck in the existing security system and put forward reasonable improvement plans and proposal, the Petri net model and the Markov Chain are introduced in this paper. This paper uses data collected by transportation Security Agency (TSA), assuming the data can represent the average level of all airports in the Unites States, to analysis the performance of security check system. By calculating the busy probabilities and the utilization probabilities, the bottleneck is found. Moreover, recommendation is given based on the parameters’ modification in Petri net 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.
A model for net migration between the Portuguese regions
Martinho, Vítor João Pereira Domingues
2011-01-01
We built a model identifying the determinants that affect the mobility of labor. The empirical part of the work will be performed for the NUTS II of Portugal, from 1996 to 2002. As main conclusion it can be said, for the NUTS II (1996-2002), which is confirmed the existence of some labor mobility in Portugal and that regional mobility is mainly influenced positively by the output growth and negatively by the unemployment rates and by the weight of the agricultural sector (1)(Martinho, 2011).
A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs
Directory of Open Access Journals (Sweden)
Merlijn Blaauw
2017-12-01
Full Text Available We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.
A reformulation of the Cost Plus Net Value Change (C+NVC) model of wildfire economics
Geoffrey H. Donovan; Douglas B. Rideout
2003-01-01
The Cost plus Net Value Change (C+NVC) model provides the theoretical foundation for wildland fire economics and provides the basis for the National Fire Management Analysis System (NFMAS). The C+NVC model is based on the earlier least Cost plus Loss model (LC+L) expressed by Sparhawk (1925). Mathematical and graphical analysis of the LC+L model illustrates two errors...
ezBioNet: A modeling and simulation system for analyzing biological reaction networks
Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo
2012-10-01
To achieve robustness against living environments, a living organism is composed of complicated regulatory mechanisms ranging from gene regulations to signal transduction. If such life phenomena are to be understand, an integrated analysis tool that should have modeling and simulation functions for biological reactions, as well as new experimental methods for measuring biological phenomena, is fundamentally required. We have designed and implemented modeling and simulation software (ezBioNet) for analyzing biological reaction networks. The software can simultaneously perform an integrated modeling of various responses occurring in cells, ranging from gene expressions to signaling processes. To support massive analysis of biological networks, we have constructed a server-side simulation system (VCellSim) that can perform ordinary differential equations (ODE) analysis, sensitivity analysis, and parameter estimates. ezBioNet integrates the BioModel database by connecting the european bioinformatics institute (EBI) servers through Web services APIs and supports the handling of systems biology markup language (SBML) files. In addition, we employed eclipse RCP (rich client platform) which is a powerful modularity framework allowing various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool, as well as a simulation system, to understand the control mechanism by monitoring the change of each component in a biological network. A researcher may perform the kinetic modeling and execute the simulation. The simulation result can be managed and visualized on ezBioNet, which is freely available at http://ezbionet.cbnu.ac.kr.
An Efficient Stock Recommendation Model Based on Big Order Net Inflow
Directory of Open Access Journals (Sweden)
Yang Yujun
2016-01-01
Full Text Available In general, the stock trend is mainly driven by the big order transactions. Believing that the stock rise with a large volume is closely associated with the big order net inflow, we propose an efficient stock recommendation model based on big order net inflow in the paper. In order to compute the big order net inflow of stock, we use the M/G/1 queue system to measure all tick-by-tick transaction data. Based on an indicator of the big order net inflow of stock, we select some stocks with the higher value of the net inflow to constitute the prerecommended stock set for the target investor user. In order to recommend some stocks with which this style is familiar them to the target users, we divide lots of investors into several categories using fuzzy clustering method and we should do our best to choose stocks from the stock set once operated by those investors who are in the same category with the target user. The experiment results show that the recommended stocks have better gains during the several days after the recommended stock day and the proposed model can provide reliable investment guidance for the target investors and let them get more stock returns.
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 ...
Self-organization of neural networks
Energy Technology Data Exchange (ETDEWEB)
Clark, J.W.; Winston, J.V.; Rafelski, J.
1984-05-14
The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (brainwashing) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conducive to the simulation of memory and learning phenomena. 18 references, 2 figures.
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.
Study of the Gray Scale, Polychromatic, Distortion Invariant Neural Networks Using the Ipa Model.
Uang, Chii-Maw
Research in the optical neural network field is primarily motivated by the fact that humans recognize objects better than the conventional digital computers and the massively parallel inherent nature of optics. This research represents a continuous effort during the past several years in the exploitation of using neurocomputing for pattern recognition. Based on the interpattern association (IPA) model and Hamming net model, many new systems and applications are introduced. A gray level discrete associative memory that is based on object decomposition/composition is proposed for recognizing gray-level patterns. This technique extends the processing ability from the binary mode to gray-level mode, and thus the information capacity is increased. Two polychromatic optical neural networks using color liquid crystal television (LCTV) panels for color pattern recognition are introduced. By introducing a color encoding technique in conjunction with the interpattern associative algorithm, a color associative memory was realized. Based on the color decomposition and composition technique, a color exemplar-based Hamming net was built for color image classification. A shift-invariant neural network is presented through use of the translation invariant property of the modulus of the Fourier transformation and the hetero-associative interpattern association (IPA) memory. To extract the main features, a quadrantal sampling method is used to sampled data and then replace the training patterns. Using the concept of hetero-associative memory to recall the distorted object. A shift and rotation invariant neural network using an interpattern hetero-association (IHA) model is presented. To preserve the shift and rotation invariant properties, a set of binarized-encoded circular harmonic expansion (CHE) functions at the Fourier domain is used as the training set. We use the shift and symmetric properties of the modulus of the Fourier spectrum to avoid the problem of centering the CHE
National Research Council Canada - National Science Library
Fox, D.G; Tedeschi, L.O; Tylutki, T.P; Russell, J.B; Van Amburgh, M.E; Chase, L.E; Pell, A.N; Overton, T.R
2004-01-01
.... The Cornell Net Carbohydrate and Protein System (CNCPS) is a mathematical model to evaluate diet and animal performance that was developed from basic principles of rumen function, microbial growth, feed digestion and passage and animal physiology. By accounting for farm-specific management, environmental and feed characteristics, more ...
Description and evaluation of a net energy intake model as a function of dietary chewing index
DEFF Research Database (Denmark)
Jensen, L.M.; Markussen, B.; Nielsen, N.I.
2016-01-01
Previously, a linear relationship has been found between net energy intake (NEI) and dietary chewing index (CI) of the diet for different types of cattle. Therefore, we propose to generalize and calibrate this relationship into a new model for direct prediction of NEI by dairy cows from CI values...
2004-08-01
Architecture [SIR01]..................................................................... 87 Figure 5.2 Communication between HH, AR and DSM in .NET URDS...Shelf DCOM Distributed Component Object Model DCS Distributed Computing System DM Discovery Manager DSM Domain Security Manager EAI Enterprise...service descriptions with little or no human administrative intervention. Service Location Protocol ( SLP ) [GUT99a], JINI [SUN01a], Ninja project
Tkacz, J.; Bukowiec, A.; Doligalski, M.
2017-08-01
The paper presentes the method of modeling and implementation of concurrent controllers. Concurrent controllers are specified by Petri nets. Then Petri nets are decomposed using symbolic deduction method of analysis. Formal methods like sequent calculus system with considered elements of Thelen's algorithm have been used here. As a result, linked state machines (LSMs) are received. Each FSM is implemented using methods of structural decomposition during process of logic synthesis. The method of multiple encoding of microinstruction has been applied. It leads to decreased number of Boolean function realized by combinational part of FSM. The additional decoder could be implemented with the use of memory blocks.
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.
MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis
Directory of Open Access Journals (Sweden)
Heike Hofmann
2006-04-01
Full Text Available MetNet (http://www.botany.iastate.edu/Ã¢ÂˆÂ¼mash/metnetex/metabolicnetex.html is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis, combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simple fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development.
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.
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Liu, Fei; Heiner, Monika; Yang, Ming
2016-01-01
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
A Petri-net coordination model for an intelligent mobile robot
Wang, F.-Y.; Kyriakopoulos, K. J.; Tsolkas, A.; Saridis, G. N.
1990-01-01
The authors present a Petri net model of the coordination level of an intelligent mobile robot system (IMRS). The purpose of this model is to specify the integration of the individual efforts on path planning, supervisory motion control, and vision systems that are necessary for the autonomous operation of the mobile robot in a structured dynamic environment. This is achieved by analytically modeling the various units of the system as Petri net transducers and explicitly representing the task precedence and information dependence among them. The model can also be used to simulate the task processing and to evaluate the efficiency of operations and the responsibility of decisions in the coordination level of the IMRS. Some simulation results on the task processing and learning are presented.
Robust modelling and simulation integration of SIMIO with coloured petri nets
De La Mota, Idalia Flores; Mujica Mota, Miguel; Angel Piera, Miquel
2017-01-01
This book presents for the first time a methodology that combines the power of a modelling formalism such as colored petri nets with the flexibility of a discrete event program such as SIMIO. Industrial practitioners have seen the growth of simulation as a methodology for tacking problems in which variability is the common denominator. Practically all industrial systems, from manufacturing to aviation are considered stochastic systems. Different modelling techniques have been developed as well as mathematical techniques for formalizing the cause-effect relationships in industrial and complex systems. The methodology in this book illustrates how complexity in modelling can be tackled by the use of coloured petri nets, while at the same time the variability present in systems is integrated in a robust fashion. The book can be used as a concise guide for developing robust models, which are able to efficiently simulate the cause-effect relationships present in complex industrial systems without losing the simulat...
Threat driven modeling framework using petri nets for e-learning system.
Khamparia, Aditya; Pandey, Babita
2016-01-01
Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a normalized method for rating vulnerabilities which will be used as basis in metric definitions and calculations. A case study has been also proposed which shows the need and feasibility of using aspect oriented stochastic petri net models for threat modeling which improves reliability, consistency and robustness of the e-learning system.
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
Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model
Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda
2016-01-01
Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747
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
Automated Model-Based Testing of Role-Based Access Control Using Predicate/Transition Nets
Xu, Dianxiang; Kent, Michael; Thomas, Lijo; Mouelhi, Tejeddine; Le Traon, Yves
2015-01-01
Role-based access control is an important access control method for securing computer systems. A role-based access control policy can be implemented incorrectly due to various reasons, such as programming errors. Defects in the implementation may lead to unauthorized access and security breaches. To reveal access control defects, this paper presents a model-based approach to automated generation of executable access control tests using predicate/transition nets. Role-permission test models ar...
Modelling major projects: What are the factors that determine net social benefits?
James A Giesecke; Madden, John R.
2009-01-01
Economic impact statements are part and parcel of project proponents seeking government assistance, infrastructure, or environmental clearance. Such impact assessments are increasingly being conducted with computable general equilibrium (CGE) models. Frequently, however, CGE modellers do not report results in economic welfare terms nor give sufficient attention to the proper simulation requirements for determining net social benefits correctly. In this paper we take the example of a major min...
Threat driven modeling framework using petri nets for e-learning system
Khamparia, Aditya; Pandey, Babita
2016-01-01
Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a...
Indian Academy of Sciences (India)
Associate Professor of. Computer Science and. Automation at the Indian. Institute of Science,. Bangalore. His research interests are broadly in the areas of stochastic modeling and scheduling methodologies for future factories; and object oriented modeling. GENERAL I ARTICLE. Petri Nets. 1. Overview and Foundations.
Enabling Grid Computing resources within the KM3NeT computing model
Directory of Open Access Journals (Sweden)
Filippidis Christos
2016-01-01
Full Text Available KM3NeT is a future European deep-sea research infrastructure hosting a new generation neutrino detectors that – located at the bottom of the Mediterranean Sea – will open a new window on the universe and answer fundamental questions both in particle physics and astrophysics. International collaborative scientific experiments, like KM3NeT, are generating datasets which are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. These experiments, in their majority, adopt computing models consisting of different Tiers with several computing centres and providing a specific set of services for the different steps of data processing such as detector calibration, simulation and data filtering, reconstruction and analysis. The computing requirements are extremely demanding and, usually, span from serial to multi-parallel or GPU-optimized jobs. The collaborative nature of these experiments demands very frequent WAN data transfers and data sharing among individuals and groups. In order to support the aforementioned demanding computing requirements we enabled Grid Computing resources, operated by EGI, within the KM3NeT computing model. In this study we describe our first advances in this field and the method for the KM3NeT users to utilize the EGI computing resources in a simulation-driven use-case.
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...
Ogutu, Joseph O; Schulz-Streeck, Torben; Piepho, Hans-Peter
2012-05-21
Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. We predicted GEBVs for a quantitative trait using a dataset on 3000 progenies of 20 sires and 200 dams and an accompanying genome consisting of five chromosomes with 9990 biallelic SNP-marker loci simulated for the QTL-MAS 2011 workshop. We applied all the six methods that use penalty-based (regularization) shrinkage to handle datasets with far more predictors than observations. The lasso, elastic net and their adaptive extensions further possess the desirable property that they simultaneously select relevant predictive markers and optimally estimate their effects. The regression models were trained with a subset of 2000 phenotyped and genotyped individuals and used to predict GEBVs for the remaining 1000 progenies without phenotypes. Predictive accuracy was assessed using the root mean squared error, the Pearson correlation between predicted GEBVs and (1) the true genomic value (TGV), (2) the true breeding value (TBV) and (3) the simulated phenotypic values based on fivefold cross-validation (CV). The elastic net, lasso, adaptive lasso and the adaptive elastic net all had
BioNetGen 2.2: advances in rule-based modeling.
Harris, Leonard A; Hogg, Justin S; Tapia, José-Juan; Sekar, John A P; Gupta, Sanjana; Korsunsky, Ilya; Arora, Arshi; Barua, Dipak; Sheehan, Robert P; Faeder, James R
2016-11-01
: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible. Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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.
Modeling and performance analysis of the emergency rescue logistics system based on Petri nets
Directory of Open Access Journals (Sweden)
Mingxin YANG
2017-06-01
Full Text Available In order to more effectively analyze the performance of logistics emergency rescue system, based on analyzing the characteristics and main functions of the Petri net and its advantage of describing asynchronous concurrent random image system, this paper introduces stochastic Petri net modeling method to establish emergency system model on the basis of serious natural disasters or emergencies, and analyzes the performance. Firstly, according to the emergency rescue system of logistics flow chart, the Petri net model is built, and through the analysis of accessibility, activity and safety, the validity of the model is verified; Secondly, the Markov chain is constructed using the characteristics of isostructuralism with the Markov stochastic process, the linear equations is established, and a numerical example is introduced, reflecting the problems of the system through the quantitative analysis of main performance indexes. Finally, some suggestions are put forward about the problems. On the one hand, this modeling can vividly describe the sequential and asynchronous concurrent relationships between the processes; on the other hand, its mature performance analysis method can effectively explore the key links which have significant impacts on the operational efficiency of the system. The system analysis theory provides a feasible method for the analysis of system flow, and the proposal provides a theoretical basis for the optimization of the entire emergency logistics rescue system.
Kasimov, Oleg V.; Karchenova, Elena V.; Maximova, Irina L.
2007-05-01
The new approach to training doctors which specialty means skill to distinguish various images - for example, doctors of beam diagnostics, pathologists, hematologists is possible. Telemedicine by means of opportunities of the Internet and video-conference is capable to create expert databases in the several world centers. Neural Networks (the Programs, being a part of the Artificial Intellect) - are trained to give out variants of possible interpretations of the set image on the basis of these expert databases. And the doctors trained the above-named specialties, spend not years and not tens years for achievement of an expert level of professionalism, saving time and greater means and societies for training. Having an opportunity diagnostics at the highest level, the medicine improves quality of a life of the patient, also saving its means.
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.
Evaluation of Model Results and Measured Performance of Net-Zero Energy Homes in Hawaii: Preprint
Energy Technology Data Exchange (ETDEWEB)
Norton, P.; Kiatreungwattana, K.; Kelly, K. J.
2013-03-01
The Kaupuni community consists of 19 affordable net-zero energy homes that were built within the Waianae Valley of Oahu, Hawaii in 2011. The project was developed for the native Hawaiian community led by the Department of Hawaiian Homelands. This paper presents a comparison of the modeled and measured energy performance of the homes. Over the first year of occupancy, the community as a whole performed within 1% of the net-zero energy goals. The data show a range of performance from house to house with the majority of the homes consistently near or exceeding net-zero, while a few fall short of the predicted net-zero energy performance. The impact of building floor plan, weather, and cooling set point on this comparison is discussed. The project demonstrates the value of using building energy simulations as a tool to assist the project to achieve energy performance goals. Lessons learned from the energy performance monitoring has had immediate benefits in providing feedback to the homeowners, and will be used to influence future energy efficient designs in Hawaii and other tropical climates.
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...
Evaluation of Cyber Security and Modelling of Risk Propagation with Petri Nets
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Marcin Szpyrka
2017-02-01
Full Text Available This article presents a new method of risk propagation among associated elements. On thebasis of coloured Petri nets, a new class called propagation nets is defined. This class providesa formal model of a risk propagation. The proposed method allows for model relations betweennodes forming the network structure. Additionally, it takes into account the bidirectional relationsbetween components as well as relations between isomorphic, symmetrical components in variousbranches of the network. This method is agnostic in terms of use in various systems and it canbe adapted to the propagation model of any systems’ characteristics; however, it is intentionallyproposed to assess the risk of critical infrastructures. In this paper, as a proof of concept example, weshow the formal model of risk propagation proposed within the project Cyberspace Security ThreatsEvaluation System of the Republic of Poland. In the article, the idea of the method is presented aswell as its use case for evaluation of risk for cyber threats. With the adaptation of Petri nets, it ispossible to evaluate the risk for the particular node and assess the impact of this risk for all relatednodes including hierarchic relations of components as well as isomorphism of elements.
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.
An improved null model for assessing the net effects of multiple stressors on communities.
Thompson, Patrick L; MacLennan, Megan M; Vinebrooke, Rolf D
2017-07-28
Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our
Hierarchical Colored Petri Nets for Modeling and Analysis of Transit Signal Priority Control Systems
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Yisheng An
2018-01-01
Full Text Available In this paper, we consider the problem of developing a model for traffic signal control with transit priority using Hierarchical Colored Petri nets (HCPN. Petri nets (PN are useful for state analysis of discrete event systems due to their powerful modeling capability and mathematical formalism. This paper focuses on their use to formalize the transit signal priority (TSP control model. In a four-phase traffic signal control model, the transit detection and two kinds of transit priority strategies are integrated to obtain the HCPN-based TSP control models. One of the advantages to use these models is the clear presentation of traffic light behaviors in terms of conditions and events that cause the detection of a priority request by a transit vehicle. Another advantage of the resulting models is that the correctness and reliability of the proposed strategies are easily analyzed. After their full reachable states are generated, the boundness, liveness, and fairness of the proposed models are verified. Experimental results show that the proposed control model provides transit vehicles with better effectiveness at intersections. This work helps advance the state of the art in the design of signal control models related to the intersection of roadways.
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
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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.
The Design of a Unified Package for the Solution of Stochastic Petri Net Models.
1984-01-01
SOLUTION OF STOCHASTIC PETRI NET MODELS 12. PERSONAL AUTHOR(S) Kishor S. Trivedi, Gianfranco Ciardo, Andrea Bobbio * and Joanne Bechta Du an 13. TYPE OF...Science Duke University Andres Bobbio latituto Elettrotecnico Nauionale Galileo Ferrari Accession For Torino, Italy NTIS CRA&I DTJC TM3 r-- U:]nz2zlon-’ rd...effort was begun while Andrea Bobbio was a visiting scientist at Duke University, in the Fall semester, 1984. We also wish to acknowledge the other
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
Vosse, Theo; Kempen, Gerard
2009-12-01
We introduce a novel computer implementation of the Unification-Space parser (Vosse and Kempen in Cognition 75:105-143, 2000) in the form of a localist neural network whose dynamics is based on interactive activation and inhibition. The wiring of the network is determined by Performance Grammar (Kempen and Harbusch in Verb constructions in German and Dutch. Benjamins, Amsterdam, 2003), a lexicalist formalism with feature unification as binding operation. While the network is processing input word strings incrementally, the evolving shape of parse trees is represented in the form of changing patterns of activation in nodes that code for syntactic properties of words and phrases, and for the grammatical functions they fulfill. The system is capable, at least qualitatively and rudimentarily, of simulating several important dynamic aspects of human syntactic parsing, including garden-path phenomena and reanalysis, effects of complexity (various types of clause embeddings), fault-tolerance in case of unification failures and unknown words, and predictive parsing (expectation-based analysis, surprisal effects). English is the target language of the parser described.
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Licata, Ignazio
2010-01-01
Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and fuzzy. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, accor...
Modeling the Process of Color Image Recognition Using ART2 Neural Network
Directory of Open Access Journals (Sweden)
Todor Petkov
2015-09-01
Full Text Available This paper thoroughly describes the use of unsupervised adaptive resonance theory ART2 neural network for the purposes of image color recognition of x-ray images and images taken by nuclear magnetic resonance. In order to train the network, the pixel values of RGB colors are regarded as learning vectors with three values, one for red, one for green and one for blue were used. At the end the trained network was tested by the values of pictures and determines the design, or how to visualize the converted picture. As a result we had the same pictures with colors according to the network. Here we use the generalized net to prepare a model that describes the process of the color image recognition.
The Perspective on Data and Control Flow Analysis in Topological Functioning Models by Petri Nets
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Asnina Erika
2014-12-01
Full Text Available The perspective on integration of two mathematical formalisms, i.e., Colored Petri Nets (CPNs and Topological Functioning Model (TFM, is discussed in the paper. The roots of CPNs are in modeling system functionality. The TFM joins principles of system theory and algebraic topology, and formally bridges the solution domain with the problem domain. It is a base for further automated construction of software design models. The paper discusses a perspective on check of control and data flows in the TFM by CPNs formalism. The research result is definition of mappings from TFMs to CPNs.
Petri net-based modelling of human-automation conflicts in aviation.
Pizziol, Sergio; Tessier, Catherine; Dehais, Frédéric
2014-01-01
Analyses of aviation safety reports reveal that human-machine conflicts induced by poor automation design are remarkable precursors of accidents. A review of different crew-automation conflicting scenarios shows that they have a common denominator: the autopilot behaviour interferes with the pilot's goal regarding the flight guidance via 'hidden' mode transitions. Considering both the human operator and the machine (i.e. the autopilot or the decision functions) as agents, we propose a Petri net model of those conflicting interactions, which allows them to be detected as deadlocks in the Petri net. In order to test our Petri net model, we designed an autoflight system that was formally analysed to detect conflicting situations. We identified three conflicting situations that were integrated in an experimental scenario in a flight simulator with 10 general aviation pilots. The results showed that the conflicts that we had a-priori identified as critical had impacted the pilots' performance. Indeed, the first conflict remained unnoticed by eight participants and led to a potential collision with another aircraft. The second conflict was detected by all the participants but three of them did not manage the situation correctly. The last conflict was also detected by all the participants but provoked typical automation surprise situation as only one declared that he had understood the autopilot behaviour. These behavioural results are discussed in terms of workload and number of fired 'hidden' transitions. Eventually, this study reveals that both formal and experimental approaches are complementary to identify and assess the criticality of human-automation conflicts. Practitioner Summary: We propose a Petri net model of human-automation conflicts. An experiment was conducted with general aviation pilots performing a scenario involving three conflicting situations to test the soundness of our formal approach. This study reveals that both formal and experimental approaches
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.
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...
A methodological approach for using high-level Petri Nets to model the immune system response.
Pennisi, Marzio; Cavalieri, Salvatore; Motta, Santo; Pappalardo, Francesco
2016-12-22
Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra
Role of SDF1/CXCR4 Interaction in Experimental Hemiplegic Models with Neural Cell Transplantation
Directory of Open Access Journals (Sweden)
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.
Testing of information condensation in a model reverberating spiking neural network.
Vidybida, Alexander
2011-06-01
Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.
Directory of Open Access Journals (Sweden)
Susan Lambert
2005-11-01
Full Text Available There is widespread support for chronic condition management (CCM programs that require a multi-disciplinary, care-team approach. Implementation of such programs represents a paradigm shift in primary care service delivery and has significant resource implications for the general practice. Integral to the widespread uptake of care-team based CCM is information collection, storage and dissemination amongst the care-team members. This paper looks to ebusiness models for assistance in understanding the requirements of general practitioners (GPs in providing multi-disciplinary team care to patients with chronic conditions. The role required of GPs in chronic condition management is compared to that of a value net integrator. The essential characteristics of value net integrators are identified and compared to those of GPs providing multi-disciplinary team care to patients with chronic conditions. It is further suggested that a shared infrastructure is required.
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).
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.
DEFF Research Database (Denmark)
Tagesson, Håkan Torbern; Smith, Benjamin; Løfgren, Anders
2009-01-01
and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average......The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with processbased vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs...... in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model...
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.
Vasenev, Viacheslav; Stoorvogel, Jetse; Leemans, Rik; Valentini, Riccardo
2016-04-01
Urbanization is responsible for large environmental changes worldwide. Urbanization was traditionally related to negative environmental impacts, but recent research highlights the potential to store soil carbon (C) in urban areas. The net effect of urbanization on soil C is, however, poorly understood. Negative influences of construction and soil sealing can be compensated by establishing of green areas. We explored possible net effects of future urbanization on soil C-stocks in the Moscow Region. Urbanization was modelled as a function of environmental, socio-economic and neighbourhood factors. This yielded three alternative scenarios: i) including neighbourhood factors; ii) excluding neighbourhood factors and focusing on environmental drivers; and iii) considering the New Moscow Project, establishing 1500km2 of new urbanized area following governmental regulation. All three scenarios showed substantial urbanization on 500 to 2000km2 former forests and arable lands. Our analysis shows a positive net effect on SOC stocks of 5 to 11 TgC. The highest increase occurred on the less fertile Orthic Podzols and Eutric Podzoluvisols, whereas C-storage in Orthic Luvisols, Luvic Chernozems, Dystric Histosols and Eutric Fluvisols increased less. Subsoil C-stocks were much more affected with an extra 4 to 10 TgC than those in the topsoils. The highest increase of both topsoil and subsoil C stocks occurred in the New Moscow scenario with the highest urbanization. Even when the relatively high uncertainties of the absolute C-values are considered, a clear positive net effect of urbanization on C-stocks is apparent. This highlights the potential of cities to enhance C-storage. This will progressively become more important in the future following the increasing world-wide urbanization.
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.
Modeling and Analysis of Train Rear-End Collision Accidents Based on Stochastic Petri Nets
Directory of Open Access Journals (Sweden)
Chao Wu
2015-01-01
Full Text Available We proposed a model of the train rear-end collision accidents based on stochastic Petri nets (SPN theory. By isomorphic Markov chain model of the proposed accident model, we provide the quantitative analysis of the train rear-end collision accidents. Fuzzy random method is also applied to analyze the performance of the proposed model. In addition, according to the data extracted from a large amount of historical data of the accident statistics, we present a case analysis and discussion. It showed that the results of the proposed train rear-end accident model based on SPN are reasonable in practical applications and can be used to effectively analyze the accidents and prevent loss, and the results may be a reference to the department of railway safety management.
Tutorials on Agent-based Modelling with NetLogo and Network Analysis with Pajek
Berryman, Matthew J.; Angus, Simon D.
Complex adaptive systems typically contain multiple, heterogeneous agents, with non-trivial interactions. They tend to produce emergent (larger-scale) phenomena. Agent-based modelling allows one to readily capture the behaviour of a group of heterogeneous agents (such as people, animals, et cetera), with diverse behaviour and important interactions, so it is a natural fit to modelling complex systems. Many complex systems (and agent-based models thereof) can be thought of as containing networks, either explicitly or implicitly. Therefore for complex systems research it is important to have a good understanding of network analysis techniques. This chapter is aimed at beginners to complex systems modelling and network analysis, using NetLogo (Section 10.1) and Pajek (Section 10.2) respectively. It is also aimed at more advanced complex systems modellers who want an introduction to these platforms.
A Petri Net-based Approach to Reconfigurable Manufacturing Systems Modeling
Directory of Open Access Journals (Sweden)
Brian Rodrigues
2009-02-01
Full Text Available Reconfigurable manufacturing systems (RMSs have been used to provide manufacturing companies with the required capacities and capabilities, when needed. Recognizing (1 the importance of dynamic modeling and visualization in decision making support in RMSs and (2 the limitations of the existing studies, we model RMSs based on Petri net (PN techniques with focus on the process of reconfiguring system elements while considering constraints and system performance. In response to the modeling difficulties identified, a new formalism of colored timed PNs is introduced. In conjunction with colored tokens and timing in colored PNs and timed PNs, we further define a reconfiguration mechanism to meet the modeling difficulties. A case study of an electronics product is reported as an application of the proposed colored timed PNs to RMS modeling.
Model petri net of adaptive traffic lights and its collaboration with a special event
Directory of Open Access Journals (Sweden)
Tristono Tomi
2018-01-01
Full Text Available Traffic lights have an important role as the system control of vehicles flow on the urban network. Commonly, most countries still using fixed time strategy. Our research proposes the adaptive traffic lights model to response the traffic demand. It uses basic Petri net as a general modeling framework. Foractuating method of minimum and maximum green signal time interval, the green traffic lights have three-time extension units. Next, we collaborate on a case of the existence of railways that crosses on the southern arm of an intersection. We introduce both of collaboration model design of traffic lights and the railway's gate which always closes while a train passing. Verification and validation of the model are based on the simulation result of vehicles queue. The collaboration model design of traffic lights has excellent performance, and it can resolve the congestion problem better than conventional schedule.
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.
2012-01-01
Background Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. Methods We predicted GEBVs for a quantitative trait using a dataset on 3000 progenies of 20 sires and 200 dams and an accompanying genome consisting of five chromosomes with 9990 biallelic SNP-marker loci simulated for the QTL-MAS 2011 workshop. We applied all the six methods that use penalty-based (regularization) shrinkage to handle datasets with far more predictors than observations. The lasso, elastic net and their adaptive extensions further possess the desirable property that they simultaneously select relevant predictive markers and optimally estimate their effects. The regression models were trained with a subset of 2000 phenotyped and genotyped individuals and used to predict GEBVs for the remaining 1000 progenies without phenotypes. Predictive accuracy was assessed using the root mean squared error, the Pearson correlation between predicted GEBVs and (1) the true genomic value (TGV), (2) the true breeding value (TBV) and (3) the simulated phenotypic values based on fivefold cross-validation (CV). Results The elastic net, lasso, adaptive lasso and the
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.
Modeling and Optimizing Energy Utilization of Steel Production Process: A Hybrid Petri Net Approach
Directory of Open Access Journals (Sweden)
Peng Wang
2013-01-01
Full Text Available The steel industry is responsible for nearly 9% of anthropogenic energy utilization in the world. It is urgent to reduce the total energy utilization of steel industry under the huge pressures on reducing energy consumption and CO2 emission. Meanwhile, the steel manufacturing is a typical continuous-discrete process with multiprocedures, multiobjects, multiconstraints, and multimachines coupled, which makes energy management rather difficult. In order to study the energy flow within the real steel production process, this paper presents a new modeling and optimization method for the process based on Hybrid Petri Nets (HPN in consideration of the situation above. Firstly, we introduce the detailed description of HPN. Then the real steel production process from one typical integrated steel plant is transformed into Hybrid Petri Net model as a case. Furthermore, we obtain a series of constraints of our optimization model from this model. In consideration of the real process situation, we pick the steel production, energy efficiency and self-made gas surplus as the main optimized goals in this paper. Afterwards, a fuzzy linear programming method is conducted to obtain the multiobjective optimization results. Finally, some measures are suggested to improve this low efficiency and high whole cost process structure.
2009-04-01
This study details the development of a series of enhancements to the Trip Reduction Impacts of : Mobility Management Strategies (TRIMMS) model. TRIMMS allows quantifying the net social : benefits of a wide range of transportation demand management...
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.
Zhang, Jian-Hua; Xia, Jia-Jun; Garibaldi, Jonathan M; Groumpos, Petros P; Wang, Ru-Bin
2017-06-01
In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN
Galeazzi, Juan M.; Navajas, Joaquin; Mender, Bedeho M. W.; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M.
2016-01-01
ABSTRACT Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw....
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
High performance hybrid functional Petri net simulations of biological pathway models on CUDA.
Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru
2011-01-01
Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.
Petri Net Model for Serious Games Based on Motivation Behavior Classification
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Moh. Aries Syufagi
2013-01-01
Full Text Available Petri nets are graphical and mathematical tool for modeling, analyzing, and designing discrete event applicable to many systems. They can be applied to game design too, especially to design serous game. This paper describes an alternative approach to the modeling of serious game systems and classification of motivation behavior with Petri nets. To assess the motivation level of player ability, this research aims at Motivation Behavior Game (MBG. MBG improves this motivation concept to monitor how players interact with the game. This modeling employs Learning Vector Quantization (LVQ for optimizing the motivation behavior input classification of the player. MBG may provide information when a player needs help or when he wants a formidable challenge. The game will provide the appropriate tasks according to players’ ability. MBG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the players are emotionally stable. Interest of the players strongly supports the procedural learning in a serious game.
Agent-based modeling of the immune system: NetLogo, a promising framework.
Chiacchio, Ferdinando; Pennisi, Marzio; Russo, Giulia; Motta, Santo; Pappalardo, Francesco
2014-01-01
Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.
Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework
Directory of Open Access Journals (Sweden)
Ferdinando Chiacchio
2014-01-01
Full Text Available Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.
DEFF Research Database (Denmark)
Lorentsen, Louise; Tuovinen, Antti-Pekka; Xu, Jianli
2002-01-01
This paper describes the first results of a project on modelling of important feature interaction patterns of Nokia mobile phones using Coloured Petri Nets. A modern mobile phone supports many features: voice and data calls, text messaging, personal information management (phonebook and calendar)...... successfully identified inconsistencies in the specifications. Furthermore, the construction of the CPN model has lead to interesting ideas for possible improvements in the architecture of the mobile phone UI software system.......This paper describes the first results of a project on modelling of important feature interaction patterns of Nokia mobile phones using Coloured Petri Nets. A modern mobile phone supports many features: voice and data calls, text messaging, personal information management (phonebook and calendar......), WAP browsing, games, etc. All these features are packaged into a handset with a small screen and a special purpose keypad. The limited user interface and the seamless intertwining of logically separate features cause many problems in the software development of the user interface of mobile phones...
Prouty, R.; Radov, A.; Halem, M.; Nearing, G. S.
2016-12-01
Investigations of mid to high latitude atmospheric CO2 show a growing seasonal amplitude. Land surface models poorly predict net ecosystem exchange (NEE) and are unable to substantiate these sporadic observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. A global, seasonal investigation of CO2-flux is then necessary in order to assimilate into land surface models for improving the prediction of annual NEE. The Atmospheric Radiation Measurement program (ARM) of DOE collects CO2-flux measurements (in addition to CO2 concentration and various other meteorological quantities) at several towers located around the globe at half hour temporal frequencies. CO2-fluxes are calculated via the eddy covariance technique, which utilizes CO2-densities and wind velocities to calculate CO2-fluxes. The global coverage of CO2 concentrations as provided by the Orbiting Carbon Observatory (OCO-2) provide satellite-derived CO2 concentrations all over the globe. A framework relating the satellite-inferred CO2 concentrations collocated with the ground-based ARM as well as Ameriflux stations would enable calculations of CO2-fluxes far from the station sites around the entire globe. Regression techniques utilizing deep-learning neural networks may provide such a framework. Additionally, meteorological reanalysis allows for the replacement of the ARM multivariable meteorological variables needed to infer the CO2-fluxes. We present the results of inferring CO2-fluxes from OCO-2 CO2 concentrations for a two year period, Sept. 2014- Sept. 2016 at the ARM station located near Oklahoma City. A feed-forward neural network (FFNN) is used to infer relationships between the following data sets: F([ARM CO2-density], [ARM Meteorological Data]) = [ARM CO2-Flux] F([OCO-2 CO2-density],[ARM Meteorological Data]) = [ARM CO2-Flux] F([ARM CO2-density],[Meteorological Reanalysis]) = [ARM CO2-Flux
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.
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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.
Petri Nets Based Modelling of Control Flow for Memory-Aid Interactive Programs in Telemedicine
Khoromskaia, V K
2004-01-01
Petri Nets (PN) based modelling of the control flow for the interactive memory assistance programs designed for personal pocket computers and having special requirements for robustness is considered. The proposed concept allows one to elaborate the programs which can give users a variety of possibilities for a day-time planning in the presence of environmental and time restrictions. First, a PN model for a known simple algorithm is constructed and analyzed using the corresponding state equations and incidence matrix. Then a PN graph for a complicated algorithm with overlapping actions and choice possibilities is designed, supplemented by an example of its analysis. Dynamic behaviour of this graph is tested by tracing of all possible paths of the flow of control using the PN simulator. It is shown that PN based modelling provides reliably predictable performance of interactive algorithms with branched structures and concurrency requirements.
Dependency Modeling of a SOA Based System Through Colored Petri Nets
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Pawan Kumar
2016-09-01
Full Text Available Dependency relationships play an important role in testing, maintenance and configuration management of software systems. The informal dependency representations fail to observe behavioral connections among subsystems and cause ambiguity in representing different types of dependency relationships. Therefore, dependency in a software system requires a formal and unambiguous representation so that its correct effects can be visualized. In this paper, we present a Colored Petri Net based dependency analysis of a Service Oriented Architecture (SOA based system that represents specification of dependency relationships and models the dependencies in a SOA based system at conceptual level. Different types of dependency relations are represented in a formal manner by using Service Algebra. A module SOA based system ‘Online Bookshop’ has been developed and used for the purpose of modeling and example demonstration. Such modeling can help in identification of inconsistency among services, and web services can be verified for safety and reliability.
Directory of Open Access Journals (Sweden)
Samar Hayat Khan Tareen
Full Text Available Circadian rhythms are certain periodic behaviours exhibited by living organism at different levels, including cellular and system-wide scales. Recent studies have found that the circadian rhythms of several peripheral organs in mammals, such as the liver, are able to entrain their clocks to received signals independent of other system level clocks, in particular when responding to signals generated during feeding. These studies have found SIRT1, PARP1, and HSF1 proteins to be the major influencers of the core CLOCKBMAL1:PER-CRY circadian clock. These entities, along with abstracted feeding induced signals were modelled collectively in this study using Petri Nets. The properties of the model show that the circadian system itself is strongly robust, and is able to continually evolve. The modelled feeding regimens suggest that the usual 3 meals/day and 2 meals/day feeding regimens are beneficial with any more or less meals/day negatively affecting the system.
Estimating the carrying capacity of green mussel cultivation by using net nutrient removal model.
Srisunont, Chayarat; Babel, Sandhya
2016-11-15
This study aims to evaluate the nutrient removal potential and carrying capacity of green mussel cultivation by using the mass balance model. The developed model takes into consideration the green mussel growth rate, density and chlorophyll a concentration. The data employed in this study were based on culture conditions at Sriracha Fisheries Research Station, Thailand. Results show that net nutrient removal by green mussel is 3302, 380, and 124mg/year/indv for carbon, nitrogen, and phosphorus respectively. The carrying capacity of green mussel cultivation was found to be 300indv/m(2) based on chlorophyll a concentration which will not release phosphorus in the water environment beyond the standard (45μg-PO4(-3)-P/L). Higher chlorophyll a concentration results in lowered green mussel carrying capacity. This model can assist farm operators with possible management strategies for a sustainable mussel cultivation and protection of the marine environment. Copyright © 2016 Elsevier Ltd. All rights reserved.
The Models of a General Component of a Distributed Computing Systems Based On Stochastic Petri Nets
Serhan, Sami [سامي سرحان; Al-Qadi, Ziad
1994-01-01
In this paper we propose models of a distributed computing system (DCS) based on general stochastic Petri nets (GSPN). We also propose the means to decrease the number of states. We indicate that the complexity of DCS doesn't affect the models structure (a number of tokens is changed only). In this paper we also propose models for investigation of processes transfer, data interchange, and also the priority interchange. نطرح في هذا البحث نماذج لنظام حاسوب موزع تعتمد على شبكات بتري العشوائية...
Del Grosso, S. J.; Parton, W. J.; Ojima, D. S.; Mosier, A. R.; Mosier, A. R.; Paustian, K.; Peterson, G. A.
2001-12-01
We present maps showing regional patterns of land use change and soil C levels in the US Great Plains during the 20th century and time series of net greenhouse gas fluxes associated with different land uses. Net greenhouse gas fluxes were calculated by accounting for soil CO2 fluxes, the CO2 equivalents of N2O emissions and CH4 uptake, and the CO2 costs of N fertilizer production. Both historical and modern agriculture in this region have been net sources of greenhouse gases. The primary reason for this, prior to 1950, is that agriculture mined soil C and resulted in net CO2 emissions. When chemical N fertilizer became widely used in the 1950's agricultural soils began to sequester CO2-C but these soils were still net greenhouse gas sources if the effects of increased N2O emissions and decreased CH4 uptake are included. The sensitivity of net greenhouse gas fluxes to conventional and alternative land uses was explored using the DAYCENT ecosystem model. Model projections suggest that conversion to no-till, reduction of the fallow period, and use of nitrification inhibitors can significantly decrease net greenhouse gas emissions in dryland and irrigated systems, while maintaining or increasing crop yields.
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. .
DEFF Research Database (Denmark)
Westergaard, Michael
2006-01-01
This paper introduces the notion of game coloured Petri nets. This allows the modeler to explicitly model what parts of the model comprise the modeled system and what parts are the environment of the modeled system. We give the formal definition of game coloured Petri nets, a means of reachability...... analysis of this net class, and an application of game coloured Petri nets to automatically generate easy-to-understand visualizations of the model by exploiting the knowledge that some parts of the model are not interesting from a visualization perspective (i.e. they are part of the environment...
NNR-MORVEL56: No-net-rotation model of geologically current plate motions
Argus, D.; Gordon, R. G.; Demets, C.; Zheng, L.
2010-12-01
We determine NNR-MORVEL56, a set of estimates of the velocities of 56 plates relative to the reference frame yielding no net rotation of the plates. We furthermore determine the full 168 by 168 covariance matrix describing uncertainties in the 56 velocities. 25 plate velocities are from geologically current plate motion model MORVEL [DeMets et al. 2010]; 31 plate velocities are from Bird [2003]. We calculate the 31 plate velocities relative to the MORVEL plates in the same manner by which Bird [2003] calculated them relative to NUVEL-1A. Because the 31 plates from Bird [2003] comprise just 2.8% of Earth’s surface, NNR-MORVEL56, they contribute insignificantly to the no-net-rotation frame; The estimates of the 31 plates from Bird [2003] relative to the 25 plates in MORVEL are nevertheless useful. The velocity of the Pacific plate differs between NNR-MORVEL56 and hotspot model HS3B-MORVEL [Zheng et al., abstract in GP session] on plate motion and reference frames] by a highly significant 0.34 °/Myr, which is up to 38 mm/y on Earth’s surface. This difference is 23 per cent smaller than our prior estimate of the difference from NNR-NUVEL1A [Argus and Gordon 2001] and HS3-NUVEL1A [Gripp and Gordon 2002].
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.
Razzaq, Misbah; Ahmad, Jamil
2015-01-01
Internet worms are analogous to biological viruses since they can infect a host and have the ability to propagate through a chosen medium. To prevent the spread of a worm or to grasp how to regulate a prevailing worm, compartmental models are commonly used as a means to examine and understand the patterns and mechanisms of a worm spread. However, one of the greatest challenge is to produce methods to verify and validate the behavioural properties of a compartmental model. This is why in this study we suggest a framework based on Petri Nets and Model Checking through which we can meticulously examine and validate these models. We investigate Susceptible-Exposed-Infectious-Recovered (SEIR) model and propose a new model Susceptible-Exposed-Infectious-Recovered-Delayed-Quarantined (Susceptible/Recovered) (SEIDQR(S/I)) along with hybrid quarantine strategy, which is then constructed and analysed using Stochastic Petri Nets and Continuous Time Markov Chain. The analysis shows that the hybrid quarantine strategy is extremely effective in reducing the risk of propagating the worm. Through Model Checking, we gained insight into the functionality of compartmental models. Model Checking results validate simulation ones well, which fully support the proposed framework.
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
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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.
SHER: a colored petri net based random mobility model for wireless communications.
Directory of Open Access Journals (Sweden)
Naeem Akhtar Khan
Full Text Available In wireless network research, simulation is the most imperative technique to investigate the network's behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER, a novel two-dimensional (2-D Colored Petri net (CPN based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and
SHER: a colored petri net based random mobility model for wireless communications.
Khan, Naeem Akhtar; Ahmad, Farooq; Khan, Sher Afzal
2015-01-01
In wireless network research, simulation is the most imperative technique to investigate the network's behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER), a novel two-dimensional (2-D) Colored Petri net (CPN) based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and rectify the errors
Modeling and Application of Vehicular Cyber Physical System Based Petri Nets
Directory of Open Access Journals (Sweden)
Lin Chen
2014-11-01
Full Text Available Mobile cyber physical system (MCPS has been a hot research area, where mobile nodes can mobile, and communicate with each other. As a typical MCPS, vehicular cyber physical system (VCPS plays an important role in intelligent transportation, especially in collision avoidance. There is no, however, a formal modeling and analysis method for VCPS. In the paper, the modeling method based Petri nets (PN is presented. Furthermore, the behavior expression analysis method is also presented which can deal with arbitrary distribution timed transitions. Finally, a case is introduced to verify the effectiveness about proposed method, and the results show that VCPS can greatly reduce the reaction time of vehicles behind when emergent accident occurs and then enhance the traffic safety.
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.
Semantic Networks and Neural Nets.
1984-06-01
TRAGEDIES . If John likes SCIENCE-FICTION more than SHAKESPEAREAN - TRAGEDIES then it is easy to see how SCIENCE-FICTION will be chosen as the answer...manner it is easy to see how in subsequent steps the fod may converge to [LITERARY-KIND ’AI with the choices being SCIENCE-FICTION and SHAKESPEAREAN
netCDF Operators for Rapid Analysis of Measured and Modeled Swath-like Data
Zender, C. S.
2015-12-01
Swath-like data (hereafter SLD) are defined by non-rectangular and/or time-varying spatial grids in which one or more coordinates are multi-dimensional. It is often challenging and time-consuming to work with SLD, including all Level 2 satellite-retrieved data, non-rectangular subsets of Level 3 data, and model data on curvilinear grids. Researchers and data centers want user-friendly, fast, and powerful methods to specify, extract, serve, manipulate, and thus analyze, SLD. To meet these needs, large research-oriented agencies and modeling center such as NASA, DOE, and NOAA increasingly employ the netCDF Operators (NCO), an open-source scientific data analysis software package applicable to netCDF and HDF data. NCO includes extensive, fast, parallelized regridding features to facilitate analysis and intercomparison of SLD and model data. Remote sensing, weather and climate modeling and analysis communities face similar problems in handling SLD including how to easily: 1. Specify and mask irregular regions such as ocean basins and political boundaries in SLD (and rectangular) grids. 2. Bin, interpolate, average, or re-map SLD to regular grids. 3. Derive secondary data from given quality levels of SLD. These common tasks require a data extraction and analysis toolkit that is SLD-friendly and, like NCO, familiar in all these communities. With NCO users can 1. Quickly project SLD onto the most useful regular grids for intercomparison. 2. Access sophisticated statistical and regridding functions that are robust to missing data and allow easy specification of quality control metrics. These capabilities improve interoperability, software-reuse, and, because they apply to SLD, minimize transmission, storage, and handling of unwanted data. While SLD analysis still poses many challenges compared to regularly gridded, rectangular data, the custom analyses scripts SLD once required are now shorter, more powerful, and user-friendly.
The application of Petri nets in a model based risk analysis framework
Energy Technology Data Exchange (ETDEWEB)
Braathe, Christian Andre; Kolstad, Kristian; Thunem, Atoosa P-J.
2005-09-15
In January 2001 a project called CORAS was started under the Information Society Technologies (IST) Programme. The main objective for the CORAS project was to develop a practical framework for a precise, unambiguous and effective risk analysis of security critical computerised systems. CORAS aimed to adapt, refine, extend and combine methods for risk analysis and methods for object oriented modelling together with a computerized tool to build a framework for model-based risk analysis (MBRA) of security critical systems. In very general terms, the CORAS framework uses system models created by using the Unified Modelling Language (herby known as UML) notation to analyse the systems' functionality. However, UML is not the only modelling language available, and it is not the best language for detailed system modelling, as it can become very large and therefore difficult to manage. Another possibility is to complement UML modelling with modelling by means of Petri nets (herby known as PN), which is a relative powerful modelling language. One of the great advantages of PNs is that it allows the modelling of concurrency, synchronization and resource sharing behaviour of systems. PNs also provide the possibility of a formal verification and it is also useful for detection and performance analysis because the corresponding models can be simulated. Yet, the question with respect to this work is: is it possible to improve the risk analysis process suggested by the CORAS framework when using PNs on the basis of the UML models? Accordingly, the main objective of the work is to investigate whether PNs can help us to improve the risk analysis process by supporting the system modelling activity. (Author)
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.
Greater deciduous shrub abundance extends tundra peak season and increases modeled net CO2 uptake.
Sweet, Shannan K; Griffin, Kevin L; Steltzer, Heidi; Gough, Laura; Boelman, Natalie T
2015-06-01
Satellite studies of the terrestrial Arctic report increased summer greening and longer overall growing and peak seasons since the 1980s, which increases productivity and the period of carbon uptake. These trends are attributed to increasing air temperatures and reduced snow cover duration in spring and fall. Concurrently, deciduous shrubs are becoming increasingly abundant in tundra landscapes, which may also impact canopy phenology and productivity. Our aim was to determine the influence of greater deciduous shrub abundance on tundra canopy phenology and subsequent impacts on net ecosystem carbon exchange (NEE) during the growing and peak seasons in the arctic foothills region of Alaska. We compared deciduous shrub-dominated and evergreen/graminoid-dominated community-level canopy phenology throughout the growing season using the normalized difference vegetation index (NDVI). We used a tundra plant-community-specific leaf area index (LAI) model to estimate LAI throughout the green season and a tundra-specific NEE model to estimate the impact of greater deciduous shrub abundance and associated shifts in both leaf area and canopy phenology on tundra carbon flux. We found that deciduous shrub canopies reached the onset of peak greenness 13 days earlier and the onset of senescence 3 days earlier compared to evergreen/graminoid canopies, resulting in a 10-day extension of the peak season. The combined effect of the longer peak season and greater leaf area of deciduous shrub canopies almost tripled the modeled net carbon uptake of deciduous shrub communities compared to evergreen/graminoid communities, while the longer peak season alone resulted in 84% greater carbon uptake in deciduous shrub communities. These results suggest that greater deciduous shrub abundance increases carbon uptake not only due to greater leaf area, but also due to an extension of the period of peak greenness, which extends the period of maximum carbon uptake. © 2015 John Wiley & Sons Ltd.
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.
Evaluation of modelled net primary production using MODIS and landsat satellite data fusion.
Jay, Steven; Potter, Christopher; Crabtree, Robert; Genovese, Vanessa; Weiss, Daniel J; Kraft, Maggi
2016-12-01
To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R(2) of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R(2) increases to 0.82. The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.
Energy Technology Data Exchange (ETDEWEB)
Chew, S.P.; Dunnett, S.J. [Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leics (United Kingdom); Andrews, J.D. [Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leics (United Kingdom)], E-mail: j.d.andrews@lboro.ac.uk
2008-07-15
A common scenario in engineering is that of a system which operates throughout several sequential and distinct periods of time, during which the modes and consequences of failure differ from one another. This type of operation is known as a phased mission, and for the mission to be a success the system must successfully operate throughout all of the phases. Examples include a rocket launch and an aeroplane flight. Component or sub-system failures may occur at any time during the mission, yet not affect the system performance until the phase in which their condition is critical. This may mean that the transition from one phase to the next is a critical event that leads to phase and mission failure, with the root cause being a component failure in a previous phase. A series of phased missions with no maintenance may be considered as a maintenance-free operating period (MFOP). This paper describes the use of a Petri net (PN) to model the reliability of the MFOP and phased missions scenario. The model uses Monte-Carlo simulation to obtain its results, and due to the modelling power of PNs, can consider complexities such as component failure rate interdependencies and mission abandonment. The model operates three different types of PN which interact to provide the overall system reliability modelling. The model is demonstrated and validated by considering two simple examples that can be solved analytically.
Directory of Open Access Journals (Sweden)
Jing Li
2015-01-01
Full Text Available Motivated by the need for loosely coupled and asynchronous dissemination of information, message queues are widely used in large-scale application areas. With the advent of virtualization technology, cloud-based message queueing services (CMQSs with distributed computing and storage are widely adopted to improve availability, scalability, and reliability; however, a critical issue is its performance and the quality of service (QoS. While numerous approaches evaluating system performance are available, there is no modeling approach for estimating and analyzing the performance of CMQSs. In this paper, we employ both the analytical and simulation modeling to address the performance of CMQSs with reliability guarantee. We present a visibility-based modeling approach (VMA for simulation model using colored Petri nets (CPN. Our model incorporates the important features of message queueing services in the cloud such as replication, message consistency, resource virtualization, and especially the mechanism named visibility timeout which is adopted in the services to guarantee system reliability. Finally, we evaluate our model through different experiments under varied scenarios to obtain important performance metrics such as total message delivery time, waiting number, and components utilization. Our results reveal considerable insights into resource scheduling and system configuration for service providers to estimate and gain performance optimization.
Evaluation of modelled net primary production using MODIS and landsat satellite data fusion
Directory of Open Access Journals (Sweden)
Steven Jay
2016-06-01
Full Text Available Abstract Background To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. Results Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R2 of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R2 increases to 0.82. Conclusions The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.
Bahi-Jaber, Narges; Pontier, Dominique
2003-09-01
In order to improve our understanding of directly transmitted pathogens within host populations, epidemic models should take into account individual heterogeneities as well as stochastic fluctuations in individual parameters. The associated cost results in an increasing level of complexity of the mathematical models which generally lack consistent formalisms. In this paper, we demonstrate that complex epidemic models could be expressed as colored stochastic Petri nets (CSPN). CSPN is a mathematical tool developed in computer science. The concept is based on the Markov Chain theory and on a standard well codified graphical formalism. This approach presents an alternative to other computer simulation methods since it offers both a theoretical formalism and a graphical representation that facilitate the implementation, the understanding and thus the replication or modification of the model. We explain how common concepts of epidemic models--such as the incidence function--can be easily translated into an individual based point of view in the CSPN formalism. We then illustrate this approach by using the well documented susceptible-infected model with recruitment and death.
EcoCasting: Using NetLogo models of aquatic ecosystems to teach scientific inquiry
Buzby, C. K.; Jona, K.
2010-12-01
The EcoCasting project from the Office of STEM Education Partnerships (OSEP) at Northwestern University has developed a computer model-based curriculum for high school environmental science classes to study complexity in aquatic ecosystems. EcoCasting aims to deliver cutting edge scientific research on bioaccumulation in invaded Great Lakes food webs to high school classes. Scientists and environmental engineers at Northwestern are investigating unusual bioaccumulation patterns in invaded food webs of the Great Lakes. High school students are exploring this authentic data to understand what is causing the anomalies in the data. Students use a series of NetLogo agent-based models of an aquatic ecosystem to study how toxins accumulate in the food web. Using these models, students learn about predator-prey relationships, bioaccumulation, and invasive species. Students are confronted with contradictory data collected by scientists and investigate alternative food web mechanisms at work. By studying the individual variables, students learn common scientific principles. When multiple variables are combined in a unifying model, students learn that the interactions lead to unexpected outcomes. Students learn about the complexity of the ecosystem and gain proficiency interpreting computer models and scientific data collection in this curriculum. Model of aquatic food chain
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.
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%).
A business case evaluation of workplace engineering noise control: a net-cost model.
Lahiri, Supriya; Low, Colleen; Barry, Michael
2011-03-01
This article provides a convenient tool for companies to determine the costs and benefits of alternative interventions to prevent noise-induced hearing loss (NIHL). Contextualized for Singapore and in collaboration with Singapore's Ministry of Manpower, the Net-Cost model evaluates costs of intervention for equipment and labor, avoided costs of productivity losses and medical care, and productivity gains from the employer's economic perspective. To pilot this approach, four case studies are presented, with varying degrees of economic benefits to the employer, including one in which multifactor productivity is the main driver. Although compliance agencies may not require economic analysis of NIHL, given scarce resources in a market-driven economy, this tool enables stakeholders to understand and compare the costs and benefits of NIHL interventions comprehensively and helps in determining risk management strategies.
Fluid Petri Nets and hybrid model-checking: a comparative case study
Energy Technology Data Exchange (ETDEWEB)
Gribaudo, M.; Horvath, A.; Bobbio, A.; Tronci, E.; Ciancamerla, E.; Minichino, M
2003-09-01
The modeling and analysis of hybrid systems is a recent and challenging research area which is actually dominated by two main lines: a functional analysis based on the description of the system in terms of discrete state (hybrid) automata (whose goal is to ascertain conformity and reachability properties), and a stochastic analysis (whose aim is to provide performance and dependability measures). This paper investigates a unifying view between formal methods and stochastic methods by proposing an analysis methodology of hybrid systems based on Fluid Petri Nets (FPNs). FPNs can be analyzed directly using appropriate tools. Our paper shows that the same FPN model can be fed to different functional analyzers for model checking. In order to extensively explore the capability of the technique, we have converted the original FPN into languages for discrete as well as hybrid as well as stochastic model checkers. In this way, a first comparison among the modeling power of well known tools can be carried out. Our approach is illustrated by means of a 'real world' hybrid system: the temperature control system of a co-generative plant.
A kinetic model for estimating net photosynthetic rates of cos lettuce leaves under pulsed light.
Jishi, Tomohiro; Matsuda, Ryo; Fujiwara, Kazuhiro
2015-04-01
Time-averaged net photosynthetic rate (P n) under pulsed light (PL) is known to be affected by the PL frequency and duty ratio, even though the time-averaged photosynthetic photon flux density (PPFD) is unchanged. This phenomenon can be explained by considering that photosynthetic intermediates (PIs) are pooled during light periods and then consumed by partial photosynthetic reactions during dark periods. In this study, we developed a kinetic model to estimate P n of cos lettuce (Lactuca sativa L. var. longifolia) leaves under PL based on the dynamics of the amount of pooled PIs. The model inputs are average PPFD, duty ratio, and frequency; the output is P n. The rates of both PI accumulation and consumption at a given moment are assumed to be dependent on the amount of pooled PIs at that point. Required model parameters and three explanatory variables (average PPFD, frequency, and duty ratio) were determined for the simulation using P n values under PL based on several combinations of the three variables. The model simulation for various PL levels with a wide range of time-averaged PPFDs, frequencies, and duty ratios further demonstrated that P n under PL with high frequencies and duty ratios was comparable to, but did not exceed, P n under continuous light, and also showed that P n under PL decreased as either frequency or duty ratio was decreased. The developed model can be used to estimate P n under various light environments where PPFD changes cyclically.
Energy Technology Data Exchange (ETDEWEB)
Wang, Peijuan; Xie, Donghui; Zhou, Yuyu; E, Youhao; Zhu, Qijiang
2014-01-16
The ecological structure in the arid and semi-arid region of Northwest China with forest, grassland, agriculture, Gobi, and desert, is complex, vulnerable, and unstable. It is a challenging and sustaining job to keep the ecological structure and improve its ecological function. Net primary productivity (NPP) modeling can help to improve the understanding of the ecosystem, and therefore, improve ecological efficiency. The boreal ecosystem productivity simulator (BEPS) model provides the possibility of NPP modeling in terrestrial ecosystem, but it has some limitations for application in arid and semi-arid regions. In this paper we improve the BEPS model, in terms of its water cycle by adding the processes of infiltration and surface runoff, to be applicable in arid and semi-arid regions. We model the NPP of forest, grass, and crop in Gansu Province as an experimental area in Northwest China in 2003 using the improved BEPS model, parameterized with moderate resolution remote sensing imageries and meteorological data. The modeled NPP using improved BEPS agrees better with the ground measurements in Qilian Mountain than that with original BEPS, with a higher R2 of 0.746 and lower root mean square error (RMSE) of 46.53 gC/m2 compared to R2 of 0.662 and RMSE of 60.19 gC/m2 from original BEPS. The modeled NPP of three vegetation types using improved BEPS show evident differences compared to that using original BEPS, with the highest difference ratio of 9.21% in forest and the lowest value of 4.29% in crop. The difference ratios between different vegetation types lie on the dependence on natural water sources. The modeled NPP in five geographic zones using improved BEPS are higher than those with original BEPS, with higher difference ratio in dry zones and lower value in wet zones.
Petri net modeling and simulation of pipelined redistributions for a deadlock-free system
Directory of Open Access Journals (Sweden)
Stavros I. Souravlas
2015-12-01
Full Text Available The growing use of multiprocessing systems has given rise to the necessity for modeling, verifying, and evaluating their performance in order to fully exploit hardware. The Petri net (PN formalism is a suitable tool for modeling parallel systems due to its basic characteristics, such as parallelism and synchronization. In addition, the PN formalism allows the incorporation of more details of the real system into the model. Examples of such details include contention for shared resources (like memory or identification of blocked processes (a definition for blocked processes is found in the Introduction section. In this paper, PNs are considered as a modeling framework to verify and study the performance of parallel pipelined communications. The main strength of the pipelines is that if organized in a proper way, they lead to overlapping of computation, communication, and read/write costs that incur in parallel communications. Most of the well-known pipelined schemes have been evaluated by theoretical analysis, queueing networks, and simulations. Usually, the factors taken into account are scheduling, message classification, and buffer spacing. To the best of our knowledge, there is no work in the literature that uses PN as a modeling tool for verification of the pipeline-based scheme. Apart from verification, a more accurate and complete model should also consider other factors, such as contentions and blocked processes. These factors have a high impact on the performance of a parallel system. The PN model presented in this paper accurately captures the behavior of the pipeline-based parallel communication system. The model considers synchronization, message scheduling, and message classification, while it is proven to be free of deadlocks and contentions. Also, the model is characterized by symmetry, so it can be used for large and complex systems.
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.)
Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi
2018-02-02
Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity
MONALISA for stochastic simulations of Petri net models of biochemical systems.
Balazki, Pavel; Lindauer, Klaus; Einloft, Jens; Ackermann, Jörg; Koch, Ina
2015-07-10
The concept of Petri nets (PN) is widely used in systems biology and allows modeling of complex biochemical systems like metabolic systems, signal transduction pathways, and gene expression networks. In particular, PN allows the topological analysis based on structural properties, which is important and useful when quantitative (kinetic) data are incomplete or unknown. Knowing the kinetic parameters, the simulation of time evolution of such models can help to study the dynamic behavior of the underlying system. If the number of involved entities (molecules) is low, a stochastic simulation should be preferred against the classical deterministic approach of solving ordinary differential equations. The Stochastic Simulation Algorithm (SSA) is a common method for such simulations. The combination of the qualitative and semi-quantitative PN modeling and stochastic analysis techniques provides a valuable approach in the field of systems biology. Here, we describe the implementation of stochastic analysis in a PN environment. We extended MONALISA - an open-source software for creation, visualization and analysis of PN - by several stochastic simulation methods. The simulation module offers four simulation modes, among them the stochastic mode with constant firing rates and Gillespie's algorithm as exact and approximate versions. The simulator is operated by a user-friendly graphical interface and accepts input data such as concentrations and reaction rate constants that are common parameters in the biological context. The key features of the simulation module are visualization of simulation, interactive plotting, export of results into a text file, mathematical expressions for describing simulation parameters, and up to 500 parallel simulations of the same parameter sets. To illustrate the method we discuss a model for insulin receptor recycling as case study. We present a software that combines the modeling power of Petri nets with stochastic simulation of dynamic
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.
Directory of Open Access Journals (Sweden)
Tuan Anh Nguyen
2014-01-01
Full Text Available It is important to assess availability of virtualized systems in IT business infrastructures. Previous work on availability modeling and analysis of the virtualized systems used a simplified configuration and assumption in which only one virtual machine (VM runs on a virtual machine monitor (VMM hosted on a physical server. In this paper, we show a comprehensive availability model using stochastic reward nets (SRN. The model takes into account (i the detailed failures and recovery behaviors of multiple VMs, (ii various other failure modes and corresponding recovery behaviors (e.g., hardware faults, failure and recovery due to Mandelbugs and aging-related bugs, and (iii dependency between different subcomponents (e.g., between physical host failure and VMM, etc. in a virtualized servers system. We also show numerical analysis on steady state availability, downtime in hours per year, transaction loss, and sensitivity analysis. This model provides a new finding on how to increase system availability by combining both software rejuvenations at VM and VMM in a wise manner.
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
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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.
Energy Technology Data Exchange (ETDEWEB)
Veroustraete, F.; Patyn, J. [Flemish Inst. for Technological Research, Boeretang (Belgium); Myneni, R.B.
1996-10-01
The evaluation and prediction of changes in carbon dynamics at the ecosystem level is a key issue in studies of global change. An operational concept for the determination of carbon fluxes for the Belgian territory is the goal of the presented study. The approach is based on the integration of remotely sensed data into ecosystem models in order to evaluate photosynthetic assimilation and net ecosystem exchange (NEE). Remote sensing can be developed as an operational tool to determine the fraction of absorbed photosynthetically active radiation (fPAR). A review of the methodological approach of mapping fPAR dynamics at the regional scale by means of NOAA11-AVHRR/2 data for the year 1990 is given. The processing sequence from raw radiance values to fPAR is presented. An interesting aspect of incorporating remote sensing derived fPAR in ecosystem models is the potential for modeling actual as opposed to potential vegetation. Further work should prove whether the concepts presented and the assumptions made in this study are valid.
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.
DARPA Neural Network Study: October 1987 - February 1988
1989-03-22
8217Neural Net’ Models Allen Waxman, Boston University 10-20-1987: Mobile Robots vs. Neural Navigators 01-19-1988: Motion Computation In Vision 63...34Weight." Neurodynamics The study of the generation and propagation of synchronized neural activity in biological systems. 70 Neuron The nerve cells in...Malsburg, "Frank Rosenblatt: Principles of neurodynamics : Perceptrons and the theory of brain mechanisms," in Brain Theory, (G. Palm and A. Aertsen, eds
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.
Sanaa, Adnen; Ben Abid, Samir; Boulila, Abdennacer; Messaoud, Chokri; Boussaid, Mohamed; Ben Fadhel, Najeh
2015-03-01
Hydrochory, the seed dispersal by water, is a strategy used by many aquatic and some terrestrial plants to move into areas appropriate for establishment. In this paper we model the hydrochory effects on the Tunisian island populations of Pancratium maritimum L. using colored Petri nets. Nineteen Tunisian coastal sites were considered including fourteen mainland and five island sites. The model was simulated for 400 thousand Atlantic Tunisian Current cycles (years). Snapshots of the island population's genetic makeup were taken for 50, 200 and 400 thousand years. The evolution of the obtained dendrograms showed a clear divide between the northern and southern island populations according to their estimated genetic make-up for the considered simulation durations. Hydrochory is not only with important ecological consequences, such as maintaining the populations of P. maritimum but also it may move species into areas appropriate for establishment. In this context, in situ and ex situ conservation measures of P. maritimum populations should be adopted very fast. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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.
Directory of Open Access Journals (Sweden)
Pavel Propastin
2009-07-01
Full Text Available Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation (PAR and its fraction absorbed by vegetation (fPAR, the light use efficiency (LUE and ecosystem respiration (Re. The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI obtained by the Vegetation instrument on board the Satellite Pour l’Observation de la Terra (SPOT satellite. Gross Primary Production (GPP of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR. The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The Re is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations.
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...
Vernez, David; Buchs, Didier R; Pierrehumbert, Guillaume E; Besrour, Adel
2004-12-01
Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets
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...
Nooren, P.; Prins, M.J.
2011-01-01
The new European framework directive contains a number of policy objectives in the area of net neutrality. In support of these objectives, the universal service directive includes a transparency obligation for ISPs. This paper proposes a multi-stakeholder model for the implementation of this
Chun-Tai. Lai; G. Katul; J. Butnor; M. Siqueira; D. Ellsworth; C. Maier; Kurt Johnsen; S. Mickeand; R. Oren
2002-01-01
Using a combination of model simulations and detailed measurements at a hierarchy of scales conducted at a sandhills forest site, the effect of fertilization on net ecosystem exchange (NEE) and its components in 6-year-old Pinus taeda stands was quantified. The detailed measurements, collected over a 20-d period in September and October, included gas...
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.
Gonzalez, F.; Barton, A.; Stock, C. A.
2016-02-01
Oceanic net primary production (NPP) accounts for roughly half of biological carbon fixation at the global scale, determining upper bounds for fisheries and export production. Assessing our ability to predict changes in NPP has thus major implications for the analysis of climate change impacts and for the management of living marine resources. Here, we fitted a series of reduced-dimension, linear dynamical spatiotemporal models to estimates of NPP derived from 18 years of remote sensing data and from simulations of a fully coupled, ocean-atmosphere Earth System Model (ESM). The method projects the evolution of NPP anomalies at the global scale from the time decay and interactions among a reduced set of major NPP modes of variability. This approach allowed us to assess potential limits to the predictability of NPP at the seasonal scale, and to assess the agreement in regional patterns of predictability based on remote sensing and ESM NPP estimates at large scales. The models are able to anticipate changes in NPP at lead times up to 24 months, especially in subtropical latitudes. Predictability was dominated by the decay of major modes at short time scales, with a prevalence of slow moving modes related to El Niño-Southern Oscillation and the Atlantic Multidecadal Oscillation. This decay dominated short-term predictions in both remote sensing and ESM NPP, although the modes prevailed for a longer time in ESM simulations. The dominance of short time persistence declined gradually at longer lead times as interactions between modes became more important for predicting NPP changes. These interactions were related to the propagation of major climate modes. Our approach provides a novel set of diagnostics to assess the performance of ESMs, and allowed us to identify potential regions where the prediction of NPP might lead to an improved management of living marine resources.
Nearly Net-Zero Exergy Districts as Models for Smart Energy Systems
National Research Council Canada - National Science Library
Şiir Kilkiş
2017-01-01
... Östra Sala backe, which will have a new energy concept. The latter is analysed based on proposals for two phases that aim to reach a net-zero district target based on the quality of energy (exergy...
A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.
López Puga, Jorge; García García, Juan
2012-11-01
Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.
ASAS centennial paper: net energy systems for beef cattle--concepts, application, and future models.
Ferrell, C L; Oltjen, J W
2008-10-01
Development of nutritional energetics can be traced to the 1400s. Lavoisier established relationships among O(2) use, CO(2) production and heat production in the late 1700s, and the laws of thermodynamics and law of Hess were discovered during the 1840s. Those discoveries established the fundamental bases for nutritional energetics and enabled the fundamental entity ME = retained energy + heat energy to be established. Objectives became: 1) to establish relationships between gas exchange and heat energy, 2) to devise bases for evaluation of foods that could be related to energy expenditures, and 3) to establish causes of energy expenditures. From these endeavors, the basic concepts of energy partitioning by animals were developed, ultimately resulting in the development of feeding systems based on NE concepts. The California Net Energy System, developed for finishing beef cattle, was the first to be based on retained energy as determined by comparative slaughter and the first to use 2 NE values (NE(m) and NE(g)) to describe feed and animal requirements. The system has been broadened conceptually to encompass life cycle energy requirements of beef cattle and modified by the inclusion of numerous adjustments to address factors known to affect energy requirements and value of feed to meet those needs. The current NE system remains useful but is empirical and static in nature and thus fails to capture the dynamics of energy utilization by diverse animals as they respond to changing environmental conditions. Consequently, efforts were initiated to develop dynamic simulation models that captured the underlying biology and thus were sensitive to variable genetic and environmental conditions. Development of a series of models has been described to show examples of the conceptual evolution of dynamic, mechanistic models and their applications. Generally with each new system, advances in prediction accuracy came about by adding new terms to conceptually validated models
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
Salikhmetov, Anton
2013-01-01
Draft translation to Russian of Chapter 7, Interaction-Based Models of Computation, from Models of Computation: An Introduction to Computability Theory by Maribel Fernandez. "In this chapter, we study interaction nets, a model of computation that can be seen as a representative of a class of models based on the notion of 'computation as interaction'. Interaction nets are a graphical model of computation devised by Yves Lafont in 1990 as a generalisation of the proof structures of linear logic...
Directory of Open Access Journals (Sweden)
Rui Zhang
2017-05-01
Full Text Available Estimates of regional net primary productivity (NPP are useful in modeling regional and global carbon cycles, especially in karst areas. This work developed a new method to study NPP characteristics and changes in Chongqing, a typical karst area. To estimate NPP accurately, the model which integrated an ecosystem process model (CEVSA with a light use efficiency model (GLOPEM called GLOPEM-CEVSA was applied. The fraction of photosynthetically active radiation (fPAR was derived from remote sensing data inversion based on moderate resolution imaging spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values, which were simulated by the model, matched the observed data well. The values of other relevant NPP models, as well as the MOD17A3 NPP products (NPP MOD17, were compared. In terms of spatial distribution, NPP decreased from northeast to southwest in the Chongqing region. The annual average NPP in the study area was approximately 534 gC/m2a (Std. = 175.53 from 2001 to 2011, with obvious seasonal variation characteristics. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. NPP changed with the fraction of absorbed PAR, and NPP was also significantly correlated to precipitation and temperature at monthly temporal scales, and showed stronger sensitivity to interannual variation in temperature.
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.
DEFF Research Database (Denmark)
Jensen, Kurt
1991-01-01
This paper describes how Coloured Petri Nets (CP-nets) have been developed — from being a promising theoretical model to being a full-fledged language for the design, specification, simulation, validation and implementation of large software systems (and other systems in which human beings and...... use of CP-nets — because it means that the function representation and the translations (which are a bit mathematically complex) no longer are parts of the basic definition of CP-nets. Instead they are parts of the invariant method (which anyway demands considerable mathematical skills...
Annotating Coloured Petri Nets
DEFF Research Database (Denmark)
Lindstrøm, Bo; Wells, Lisa Marie
2002-01-01
-net. An example of such auxiliary information is a counter which is associated with a token to be able to do performance analysis. Modifying colour sets and arc inscriptions in a CP-net to support a specific use may lead to creation of several slightly different CP-nets – only to support the different uses...... a method which makes it possible to associate auxiliary information, called annotations, with tokens without modifying the colour sets of the CP-net. Annotations are pieces of information that are not essential for determining the behaviour of the system being modelled, but are rather added to support...
Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems
Directory of Open Access Journals (Sweden)
Ashkan Nabavi-Pelesaraei
2016-01-01
Full Text Available This study was conducted in order to determine energy consumption, model and analyze the input–output, energy efficiencies and GHG emissions for watermelon production using artificial neural networks (ANNs in the Guilan province of Iran, based on three different farm sizes. For this purpose, the initial data was collected from 120 watermelon producers in Langroud and Chaf region, two small cities in the Guilan province. The results indicated that total average energy input for watermelon production was 40228.98 MJ ha–1. Also, chemical fertilizers (with 76.49% were the highest energy inputs for watermelon production. Moreover, the share of non-renewable energy (with 96.24% was more than renewable energy (with 3.76% in watermelon production. The rate of energy use efficiency, energy productivity and net energy was calculated as 1.29, 0.68 kg MJ−1 and 11733.64 MJ ha−1, respectively. With respect to GHG analysis, the average of total GHG emissions was calculated about 1015 kgCO2eq. ha−1. The results illustrated that share of nitrogen (with 54.23% was the highest in GHG emissions for watermelon production, followed by diesel fuel (with 16.73% and electricity (with 15.45%. In this study, Levenberg–Marquardt learning Algorithm was used for training ANNs based on data collected from watermelon producers. The ANN model with 11–10–2 structure was the best one for predicting the watermelon yield and GHG emissions. In the best topology, the coefficient of determination (R2 was calculated as 0.969 and 0.995 for yield and GHG emissions of watermelon production, respectively. Furthermore, the results of sensitivity analysis revealed that the seed and human labor had the highest sensitivity in modeling of watermelon yield and GHG emissions, respectively.
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-.
Directory of Open Access Journals (Sweden)
Hsien-Pin Hsu
2017-02-01
Full Text Available Container terminals (CTs play an essential role in the global transportation system. To deal with growing container shipments, a CT needs to better solve the three essential seaside operational problems; berth allocation problem (BAP, quay crane assignment problem (QCAP, and quay crane scheduling problem (QCSP, which affect the performance of a CT considerably. In past studies, the three seaside operational problems have often been solved individually or partially, which is likely to result in poor overall system performance. However, solving the three seaside operational problems simultaneously is in fact a very complicated task. In this research, we dealt with the three seaside operational problems at the same time by using a novel high-level Petri net, termed an Object-Oriented and Timed Predicate/Transition Net (OOTPr/Tr net. After defining the three seaside operational problems formally, we integrated them as a three-level framework that was further transformed into an OOTPr/Tr net model. Then, using the Prolog programming language, we implemented this model as a simulation tool to find the best solution based on the various combinations of heuristic rules used.
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)
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.
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
The use of artificial neural networks in experimental data acquisition and aerodynamic design
Meade, Andrew J., Jr.
1991-01-01
It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.
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