Modulated error diffusion CGHs for neural nets

Science.gov (United States)

Vermeulen, Pieter J. E.; Casasent, David P.

1990-05-01

New modulated error diffusion CGHs (computer generated holograms) for optical computing are considered. Specific attention is given to their use in optical matrix-vector, associative processor, neural net and optical interconnection architectures. We consider lensless CGH systems (many CGHs use an external Fourier transform (FT) lens), the Fresnel sampling requirements, the effects of finite CGH apertures (sample and hold inputs), dot size correction (for laser recorders), and new applications for this novel encoding method (that devotes attention to quantization noise effects).

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

Science.gov (United States)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

OpenAIRE

Visin, Francesco; Ciccone, Marco; Romero, Adriana; Kastner, Kyle; Cho, Kyunghyun; Bengio, Yoshua; Matteucci, Matteo; Courville, Aaron

2015-01-01

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally ...

NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

Science.gov (United States)

Baffes, P. T.

1994-01-01

NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

Deep neural nets as a method for quantitative structure-activity relationships.

Science.gov (United States)

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.

Face recognition: Eigenface, elastic matching, and neural nets

International Nuclear Information System (INIS)

Zhang, J.; Lades, M.

1997-01-01

This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice

tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

Science.gov (United States)

Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

2016-11-01

tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

Directory of Open Access Journals (Sweden)

Todor Petkov

2013-12-01

Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.

A new neural net approach to robot 3D perception and visuo-motor coordination

Science.gov (United States)

Lee, Sukhan

1992-01-01

A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013

International Nuclear Information System (INIS)

Dev, Priya; Martin, Michael A.

2014-01-01

Highlights: • Neural nets are unable to properly capture spiky price behavior found in the electricity market. • We modeled electricity price data from the Australian National Electricity Market over 15 years. • Neural nets need to be augmented with other modeling techniques to capture price spikes. • We fit a Generalized Pareto Distribution to price spikes using a peaks-over-thresholds approach. - Abstract: Competitors in the electricity supply industry desire accurate predictions of electricity spot prices to hedge against financial risks. Neural networks are commonly used for forecasting such prices, but certain features of spot price series, such as extreme price spikes, present critical challenges for such modeling. We investigate the predictive capacity of neural networks for electricity spot prices using Australian National Electricity Market data. Following neural net modeling of the data, we explore extreme price spikes through extreme value modeling, fitting a Generalized Pareto Distribution to price peaks over an estimated threshold. While neural nets capture the smoother aspects of spot price data, they are unable to capture local, volatile features that characterize electricity spot price data. Price spikes can be modeled successfully through extreme value modeling

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.

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.)

Neural net prediction of tokamak plasma disruptions

International Nuclear Information System (INIS)

Hernandez, J.V.; Lin, Z.; Horton, W.; McCool, S.C.

1994-10-01

The computation based on neural net algorithms in predicting minor and major disruptions in TEXT tokamak discharges has been performed. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal, measured by a single Mirnov coil. The time step used (= 0.04ms) corresponds to the experimental data sampling rate. Two kinds of approaches are adopted for the task, the contiguous future prediction and the multi-timescale prediction. Results are shown for comparison. Both networks are trained through the back-propagation algorithm with inertial terms. The degree of this success indicates that the magnetic fluctuations associated with tokamak disruptions may be characterized by a relatively low-dimensional dynamical system

Analysis of Salinity Intrusion in the San Francisco Bay-Delta Using a GA-Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

Science.gov (United States)

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

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

Unfolding code for neutron spectrometry based on neural nets technology

International Nuclear Information System (INIS)

Ortiz R, J. M.; Vega C, H. R.

2012-10-01

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 R obust Design of Artificial Neural Networks Methodology . 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 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)

Neural net generated seismic facies map and attribute facies map

International Nuclear Information System (INIS)

Addy, S.K.; Neri, P.

1998-01-01

The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

Exemplar-based optical neural net classifier for color pattern recognition

Science.gov (United States)

Yu, Francis T. S.; Uang, Chii-Maw; Yang, Xiangyang

1992-10-01

We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

Real-time applications of neural nets

International Nuclear Information System (INIS)

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

Real-time applications of neural nets

International Nuclear Information System (INIS)

Spencer, J.E.

1989-01-01

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. In this paper, such issues are considered, examples given and possibilities discussed

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.

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)

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Science.gov (United States)

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

Multiflavor string-net models

Science.gov (United States)

Lin, Chien-Hung

2017-05-01

We generalize the string-net construction to multiple flavors of strings, each of which is labeled by the elements of an Abelian group Gi. The same flavor of strings can branch, while different flavors of strings can cross one another and thus they form intersecting string nets. We systematically construct the exactly soluble lattice Hamiltonians and the ground-state wave functions for the intersecting string-net condensed phases. We analyze the braiding statistics of the low-energy quasiparticle excitations and find that our model can realize all the topological phases as the string-net model with group G =∏iGi . In this respect, our construction provides various ways of building lattice models which realize topological order G , corresponding to different partitions of G and thus different flavors of string nets. In fact, our construction concretely demonstrates the Künneth formula by constructing various lattice models with the same topological order. As an example, we construct the G =Z2×Z2×Z2 string-net model which realizes a non-Abelian topological phase by properly intersecting three copies of toric codes.

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.

Neural Net Safety Monitor Design

Science.gov (United States)

Larson, Richard R.

2007-01-01

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

Do neural nets learn statistical laws behind natural language?

Directory of Open Access Journals (Sweden)

Shuntaro Takahashi

Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

Goal-seeking neural net for recall and recognition

Science.gov (United States)

Omidvar, Omid M.

1990-07-01

Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

Turkey's net energy consumption

International Nuclear Information System (INIS)

Soezen, Adnan; Arcaklioglu, Erol; Oezkaymak, Mehmet

2005-01-01

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R 2 -value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R 2 values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies

Schema generation in recurrent neural nets for intercepting a moving target.

Science.gov (United States)

Fleischer, Andreas G

2010-06-01

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

Neural net based determination of generator-shedding requirements in electric power systems

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

1992-09-01

This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

Neural-net disruption predictor in JT-60U

International Nuclear Information System (INIS)

Yoshino, R.

2003-01-01

The prediction of major disruptions caused by the density limit, the plasma current ramp-down with high internal inductance l i , the low density locked mode and the β-limit has been investigated in JT-60U. The concept of 'stability level', newly proposed in this paper to predict the occurrence of a major disruption, is calculated from nine input parameters every 2 ms by the neural network and the start of a major disruption is predicted when the stability level decreases to a certain level, the 'alarm level'. The neural network is trained in two steps. It is first trained with 12 disruptive and six non-disruptive shots (total of 8011 data points). Second, the target output data for 12 disruptive shots are modified and the network is trained again with additional data points generated by the operator. The 'neural-net disruption predictor' obtained has been tested for 300 disruptive shots (128 945 data points) and 1008 non-disruptive shots (982 800 data points) selected from nine years of operation (1991-1999) of JT-60U. Major disruptions except for those caused by the -limit have been predicted with a prediction success rate of 97-98% at 10 ms prior to the disruption and higher than 90% at 30 ms prior to the disruption while the false alarm rate is 2.1% for non-disruptive shots. This prediction performance has been confirmed for 120 disruptive shots (56 163 data points), caused by the density limit, as well as 1032 non-disruptive shots (1004 611 data points) in the last four years of operation (1999-2002) of JT-60U. A careful selection of the input parameters supplied to the network and the newly developed two-step training of the network have reduced the false alarm rate resulting in a considerable improvement of the prediction success rate. (author)

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

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...

The principles of artificial neural network information processing

International Nuclear Information System (INIS)

Dai, Ru-Wei

1993-01-01

In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

The principles of artificial neural network information processing

International Nuclear Information System (INIS)

Dai, Ru-Wei

1993-01-01

In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

Accelerator diagnosis and control by Neural Nets

International Nuclear Information System (INIS)

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

Accelerator diagnosis and control by Neural Nets

International Nuclear Information System (INIS)

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 metaphore 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 A1 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

Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.

Directory of Open Access Journals (Sweden)

Morten Bo Johansen

Full Text Available 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 investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.

Petri neural network model for the effect of controlled thermomechanical process parameters on the mechanical properties of HSLA steels

Energy Technology Data Exchange (ETDEWEB)

Datta, S.

1999-10-01

The effect of composition and controlled thermomechanical process parameters on the mechanical properties of HSLA steels is modelled using the Widrow-Hoff's concept of training a neural net with feed-forward topology by applying Rumelhart's back propagation type algorithm for supervised learning, using a Petri like net structure. The data used are from laboratory experiments as well as from the published literature. The results from the neural network are found to be consistent and in good agreement with the experimented results. (author)

Unlearning in feed-forward multi-nets

NARCIS (Netherlands)

Spaanenburg, L; Kurkova,; Steele, NC; Neruda, R; Karny, M

2001-01-01

Multi-nets promise an improved performance over monolithic neural networks by virtue of their distributed implementation. Modular neural networks are multi-nets based on an judicious assembly of functionally different parts. This can be viewed as again a monolithic network, but with more complex

Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

Science.gov (United States)

Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

2018-02-22

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

International Nuclear Information System (INIS)

Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

2014-01-01

We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.

An adaptable Boolean net trainable to control a computing robot

International Nuclear Information System (INIS)

Lauria, F. E.; Prevete, R.; Milo, M.; Visco, S.

1999-01-01

We discuss a method to implement in a Boolean neural network a Hebbian rule so to obtain an adaptable universal control system. We start by presenting both the Boolean neural net and the Hebbian rule we have considered. Then we discuss, first, the problems arising when the latter is naively implemented in a Boolean neural net, second, the method consenting us to overcome them and the ensuing adaptable Boolean neural net paradigm. Next, we present the adaptable Boolean neural net as an intelligent control system, actually controlling a writing robot, and discuss how to train it in the execution of the elementary arithmetic operations on operands represented by numerals with an arbitrary number of digits

Deep Flare Net (DeFN) Model for Solar Flare Prediction

Science.gov (United States)

Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Ishii, M.

2018-05-01

We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS = 0.80 for ≥M-class flares and TSS = 0.63 for ≥C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.

Control of 12-Cylinder Camless Engine with Neural Networks

OpenAIRE

Ashhab Moh’d Sami

2017-01-01

The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and appl...

ConvNetQuake: Convolutional Neural Network for Earthquake Detection and Location

Science.gov (United States)

Denolle, M.; Perol, T.; Gharbi, M.

2017-12-01

Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. In this work, we leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for probabilistic earthquake detection and location from single stations. We apply our technique to study two years of induced seismicity in Oklahoma (USA). We detect 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm detection performances are at least one order of magnitude faster than other established methods.

Comparisons of a Quantum Annealing and Classical Computer Neural Net Approach for Inferring Global Annual CO2 Fluxes over Land

Science.gov (United States)

Halem, M.; Radov, A.; Singh, D.

2017-12-01

Investigations of mid to high latitude atmospheric CO2 show growing amplitudes in seasonal variations over the past several decades. Recent high-resolution satellite measurements of CO2 concentration are now available for three years from the Orbiting Carbon Observatory-2. The Atmospheric Radiation Measurement (ARM) program of DOE has been making long-term CO2-flux measurements (in addition to CO2 concentration and an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour frequencies. Recent papers have shown CO2 fluxes inferred by assimilating CO2 observations into ecosystem models are largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, new approaches for calculating CO2-flux for assimilation into land surface models are necessary for improving the prediction of annual carbon uptake. In this study, we calculate and compare the predicted CO2 fluxes results employing a Feed Forward Backward Propagation Neural Network model on two architectures, (i) an IBM Minsky Computer node and (ii) a hybrid version of the ARC D-Wave quantum annealing computer. We compare the neural net results of predictions of CO2 flux from ARM station data for three different DOE ecosystem sites; an arid plains near Oklahoma City, a northern arctic site at Barrows AL, and a tropical rainforest site in the Amazon. Training times and predictive results for the calculating annual CO2 flux for the two architectures for each of the three sites are presented. Comparative results of predictions as measured by RMSE and MAE are discussed. Plots and correlations of observed vs predicted CO2 flux are also presented for all three sites. We show the estimated training times for quantum and classical calculations when extended to calculating global annual Carbon Uptake over land. We also

Spike Neural Models Part II: Abstract Neural Models

Directory of Open Access Journals (Sweden)

Johnson, Melissa G.

2018-02-01

Full Text Available 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 which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

A Restricted Boltzman Neural Net to Infer Carbon Uptake from OCO-2 Satellite Data

Science.gov (United States)

Halem, M.; Dorband, J. E.; Radov, A.; Barr-Dallas, M.; Gentine, P.

2015-12-01

For several decades, scientists have been using satellite observations to infer climate budgets of terrestrial carbon uptake employing inverse methods in conjunction with ecosystem models and coupled global climate models. This is an extremely important Big Data calculation today since the net annual photosynthetic carbon uptake changes annually over land and removes on average ~20% of the emissions from human contributions to atmospheric loading of CO2 from fossil fuels. Unfortunately, such calculations have large uncertainties validated with in-situ networks of measuring stations across the globe. One difficulty in using satellite data for these budget calculations is that the models need to assimilate surface fluxes of CO2 as well as soil moisture, vegatation cover and the eddy covariance of latent and sensible heat to calculate the carbon fixed in the soil while satellite spectral observations only provide near surface concentrations of CO2. In July 2014, NASA successfully launched OCO-2 which provides 3km surface measurements of CO2 over land and oceans. We have collected nearly one year of Level 2 XCO2 data from the OCO-2 satellite for 3 sites of ~200 km2 at equatorial, temperate and high latitudes. Each selected site was part of the Fluxnet or ARM system with tower stations for measuring and collecting CO2 fluxes on an hourly basis, in addition to eddy transports of the other parameters. We are also planning to acquire the 4km NDVI products from MODIS and registering the data to the 3km XCO2 footprints for the three sites. We have implemented a restricted Boltzman machine on the quantum annealing D-Wave computer, a novel deep learning neural net, to be used for training with station data to infer CO2 fluxes from collocated XCO2, MODIS vegetative land cover and MERRA reanalysis surface exchange products. We will present performance assessments of the D-Wave Boltzman machine for generating XCO2 fluxes from the OCO-2 satellite observations for the 3 sites by

Development of the neural net technique for particle physics. Study of the e+e- → Z0 → γH reaction

International Nuclear Information System (INIS)

Guicheney, C.

1992-01-01

This study is concerned with the application of pattern recognition methods through neural networks to High Energy physics. Two methods, Hopfield nets and multilayer nets, are analyzed and shown to have high potential for (resp.) clusterization and classification. Hopfield nets are used for the recognition of jets occurring during the fragmentation process of the e + e - reaction. Multilayer nets are used for the whole reaction analysis. Impediments are pointed out. Associated background noise is also examined. Multilayer nets may enhance the signal to noise ratio when looking for an upper limit for the production of a Higgs boson in the expected canal, and allow for the specific study of the γ b anti b

Model and calculations for net infiltration

International Nuclear Information System (INIS)

Childs, S.W.; Long, A.

1992-01-01

In this paper a conceptual model for calculating net infiltration is developed and implemented. It incorporates the following important factors: viability of climate for the next 10,000 years, areal viability of net infiltration, and important soil/plant factors that affect the soil water budget of desert soils. Model results are expressed in terms of occurrence probabilities for time periods. In addition the variability of net infiltration is demonstrated both for change with time and differences among three soil/hydrologic units present at the site modeled

Modelling of current loads on aquaculture net cages

Science.gov (United States)

Kristiansen, Trygve; Faltinsen, Odd M.

2012-10-01

In this paper we propose and discuss a screen type of force model for the viscous hydrodynamic load on nets. The screen model assumes that the net is divided into a number of flat net panels, or screens. It may thus be applied to any kind of net geometry. In this paper we focus on circular net cages for fish farms. The net structure itself is modelled by an existing truss model. The net shape is solved for in a time-stepping procedure that involves solving a linear system of equations for the unknown tensions at each time step. We present comparisons to experiments with circular net cages in steady current, and discuss the sensitivity of the numerical results to a set of chosen parameters. Satisfactory agreement between experimental and numerical prediction of drag and lift as function of the solidity ratio of the net and the current velocity is documented.

Artificial neural net system for interactive tissue classification with MR imaging and image segmentation

International Nuclear Information System (INIS)

Clarke, L.P.; Silbiger, M.; Naylor, C.; Brown, K.

1990-01-01

This paper reports on the development of interactive methods for MR tissue classification that permit mathematically rigorous methods for three-dimensional image segmentation and automatic organ/tumor contouring, as required for surgical and RTP planning. The authors investigate a number of image-intensity based tissue- classification methods that make no implicit assumptions on the MR parameters and hence are not limited by image data set. Similarly, we have trained artificial neural net (ANN) systems for both supervised and unsupervised tissue classification

Three-dimensional neural net for learning visuomotor coordination of a robot arm.

Science.gov (United States)

Martinetz, T M; Ritter, H J; Schulten, K J

1990-01-01

An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a three-dimensional lattice consisting of the units of the neural net.

Door and cabinet recognition using convolutional neural nets and real-time method fusion for handle detection and grasping

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...

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

Science.gov (United States)

van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

2017-12-01

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-Net's 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 utilizes 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.

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

Science.gov (United States)

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.

22nd Italian Workshop on Neural Nets

CERN Document Server

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.

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.

Science.gov (United States)

Amidi, Afshine; Amidi, Shervine; Vlachakis, Dimitrios; Megalooikonomou, Vasileios; Paragios, Nikos; Zacharaki, Evangelia I

2018-01-01

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

Science.gov (United States)

Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

2013-04-01

Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Science.gov (United States)

Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

2017-10-01

Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

Neural network error correction for solving coupled ordinary differential equations

Science.gov (United States)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Study of the Gray Scale, Polychromatic, Distortion Invariant Neural Networks Using the Ipa Model.

Science.gov (United States)

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

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

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...

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...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

NET model coil test possibilities

International Nuclear Information System (INIS)

Erb, J.; Gruenhagen, A.; Herz, W.; Jentzsch, K.; Komarek, P.; Lotz, E.; Malang, S.; Maurer, W.; Noether, G.; Ulbricht, A.; Vogt, A.; Zahn, G.; Horvath, I.; Kwasnitza, K.; Marinucci, C.; Pasztor, G.; Sborchia, C.; Weymuth, P.; Peters, A.; Roeterdink, A.

1987-11-01

A single full size coil for NET/INTOR represents an investment of the order of 40 MUC (Million Unit Costs). Before such an amount of money or even more for the 16 TF coils is invested as much risks as possible must be eliminated by a comprehensive development programme. In the course of such a programme a coil technology verification test should finally prove the feasibility of NET/INTOR TF coils. This study report is almost exclusively dealing with such a verification test by model coil testing. These coils will be built out of two Nb 3 Sn-conductors based on two concepts already under development and investigation. Two possible coil arrangements are discussed: A cluster facility, where two model coils out of the two Nb 3 TF-conductors are used, and the already tested LCT-coils producing a background field. A solenoid arrangement, where in addition to the two TF model coils another model coil out of a PF-conductor for the central PF-coils of NET/INTOR is used instead of LCT background coils. Technical advantages and disadvantages are worked out in order to compare and judge both facilities. Costs estimates and the time schedules broaden the base for a decision about the realisation of such a facility. (orig.) [de

Structures and scan strategies of software net models

International Nuclear Information System (INIS)

Puhr-Westerheide, P.; Sandbaek, H.

1984-01-01

The present paper deals with some aspects of plant control and monitoring systems as used in nuclear power plants. These aspects concern executable net models to run on computers. A short survey on the nets' environment and on some net scan strategies is given. Among the strategies are the 'topologically ordered scan' and the 'signal propagation scan'. A combined method 'topologically ordered signal propagation (TOSIP) scan' will be outlined as well as a net model data structure that allows the definition of subsystems for the use of clear structuration and dischargement to distributed systems. (author)

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

. The long-wave radiation models included a physically based model, an empirical model from the literature, and a new empirical model. Both empirical models used only solar radiation as required for meteorological input. The long-wave radiation models were used with model calibration coefficients from......Daily values of net radiation are used in many applications of crop-growth modeling and agricultural water management. Measurements of net radiation are not part of the routine measurement program at many weather stations and are commonly estimated based on other meteorological parameters. Daily...... 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...

Tools and Methods for RTCP-Nets Modeling and Verification

Directory of Open Access Journals (Sweden)

Szpyrka Marcin

2016-09-01

Full Text Available RTCP-nets are high level Petri nets similar to timed colored Petri nets, but with different time model and some structural restrictions. The paper deals with practical aspects of using RTCP-nets for modeling and verification of real-time systems. It contains a survey of software tools developed to support RTCP-nets. Verification of RTCP-nets is based on coverability graphs which represent the set of reachable states in the form of directed graph. Two approaches to verification of RTCP-nets are considered in the paper. The former one is oriented towards states and is based on translation of a coverability graph into nuXmv (NuSMV finite state model. The later approach is oriented towards transitions and uses the CADP toolkit to check whether requirements given as μ-calculus formulae hold for a given coverability graph. All presented concepts are discussed using illustrative examples

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

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.

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

Science.gov (United States)

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-11-30

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. 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.

Estimação do volume de árvores utilizando redes neurais artificiais Estimate of tree volume using artificial neural nets

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

Neural network signal understanding for instrumentation

DEFF Research Database (Denmark)

Pau, L. F.; Johansen, F. S.

1990-01-01

understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

Modeling of Biometric Identification System Using the Colored Petri Nets

Science.gov (United States)

Petrosyan, G. R.; Ter-Vardanyan, L. A.; Gaboutchian, A. V.

2015-05-01

In this paper we present a model of biometric identification system transformed into Petri Nets. Petri Nets, as a graphical and mathematical tool, provide a uniform environment for modelling, formal analysis, and design of discrete event systems. The main objective of this paper is to introduce the fundamental concepts of Petri Nets to the researchers and practitioners, both from identification systems, who are involved in the work in the areas of modelling and analysis of biometric identification types of systems, as well as those who may potentially be involved in these areas. In addition, the paper introduces high-level Petri Nets, as Colored Petri Nets (CPN). In this paper the model of Colored Petri Net describes the identification process much simpler.

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.

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model

Science.gov (United States)

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

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model.

Science.gov (United States)

Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

2016-09-01

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.

Models of neural networks temporal aspects of coding and information processing in biological systems

CERN Document Server

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...

Control of 12-Cylinder Camless Engine with Neural Networks

Directory of Open Access Journals (Sweden)

Ashhab Moh’d Sami

2017-01-01

Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

Musical Audio Synthesis Using Autoencoding Neural Nets

OpenAIRE

Sarroff, Andy; Casey, Michael A.

2014-01-01

With an optimal network topology and tuning of hyperpa-\\ud rameters, artificial neural networks (ANNs) may be trained\\ud to learn a mapping from low level audio features to one\\ud or more higher-level representations. Such artificial neu-\\ud ral networks are commonly used in classification and re-\\ud gression settings to perform arbitrary tasks. In this work\\ud we suggest repurposing autoencoding neural networks as\\ud musical audio synthesizers. We offer an interactive musi-\\ud cal audio synt...

LOGIC WITH EXCEPTION ON THE ALGEBRA OF FOURIER-DUAL OPERATIONS: NEURAL NET MECHANISM OF COGNITIVE DISSONANCE REDUCING

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

Thermal photovoltaic solar integrated system analysis using neural networks

Energy Technology Data Exchange (ETDEWEB)

Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

2007-07-01

The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

Science.gov (United States)

Johnson, J L

1994-09-10

The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

ANT Advanced Neural Tool

Energy Technology Data Exchange (ETDEWEB)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-07-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

ANT Advanced Neural Tool

International Nuclear Information System (INIS)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-01-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

Neural networks for aircraft control

Science.gov (United States)

Linse, Dennis

1990-01-01

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

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...... concurrent systems and Petri nets research...

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 ...

Investment Valuation Analysis with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Hüseyin İNCE

2017-07-01

Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

Computational modeling of neural plasticity for self-organization of neural networks.

Science.gov (United States)

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.

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

Science.gov (United States)

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-01-01

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. PMID:21152409

Larger bases and mixed analog/digital neural nets

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

2D neural hardware versus 3D biological ones

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

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.

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

Science.gov (United States)

Interian, Yannet; Rideout, Vincent; Kearney, Vasant P; Gennatas, Efstathios; Morin, Olivier; Cheung, Joey; Solberg, Timothy; Valdes, Gilmer

2018-03-30

The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features. Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06. Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts. © 2018 American Association of Physicists in Medicine.

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...

High level cognitive information processing in neural networks

Science.gov (United States)

Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Application of a neural network for reflectance spectrum classification

Science.gov (United States)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

State-of-the-art of applications of neural networks in the nuclear industry

International Nuclear Information System (INIS)

Zwingelstein, G.; Masson, M.H.

1990-01-01

Artificial neural net models have been extensively studied for many years in various laboratories to try to simulate with computer programs the human brain performances. The first applications were developed in the fields of speech and image recognition. The aims of these studies were mainly to classify rapidly patterns corrupted by noises or partly missing. Neural networks with the development of new net topologies and algorithms and parallel computing hardwares and softwares are to-day very promising for applications in many industries. In the introduction, this paper presents the anticipated benefits of the uses of neural networks for industrial applications. Then a brief overview of the main neural networks is provided. Finally a short review of neural networks applications in the nuclear industry is given. It covers domains such as: predictive maintenance for vibratory surveillance of rotating machinery, signal processing, operator guidance and eddy current inspection. In conclusion recommendations are made to use with efficiency neural networks for practical applications. In particular the need for supercomputing will be pinpointed. (author)

predicting water levels at kainji dam using artificial neural networks

African Journals Online (AJOL)

2013-03-01

Mar 1, 2013 ... Apart from insufficient number of power generation plants, existing ones are ... ture and/or functional aspects of biological neural net-. Nigerian Journal of ... Their model used the radiosonde-based 700-hPa wind direction and ...

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

2008-01-01

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

Reconstruction of neutron spectra through neural networks

International Nuclear Information System (INIS)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

2003-01-01

A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

Tutorial on neural network applications in high energy physics: A 1992 perspective

International Nuclear Information System (INIS)

Denby, B.

1992-04-01

Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given

Neural network models of categorical perception.

Science.gov (United States)

Damper, R I; Harnad, S R

2000-05-01

Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

Universal approximation in p-mean by neural networks

NARCIS (Netherlands)

Burton, R.M; Dehling, H.G

A feedforward neural net with d input neurons and with a single hidden layer of n neurons is given by [GRAPHICS] where a(j), theta(j), w(ji) is an element of R. In this paper we study the approximation of arbitrary functions f: R-d --> R by a neural net in an L-p(mu) norm for some finite measure mu

Salient regions detection using convolutional neural networks and color volume

Science.gov (United States)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

Invariant visual object and face recognition: neural and computational bases, and a model, VisNet

Directory of Open Access Journals (Sweden)

Edmund T eRolls

2012-06-01

Full Text Available Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

PNet: A Python Library for Petri Net Modeling and Simulation

OpenAIRE

Zhu En Chay; Bing Feng Goh; Maurice HT Ling

2016-01-01

Petri Net is a formalism to describe changes between 2 or more states across discrete time and has been used to model many systems. We present PNet – a pure Python library for Petri Net modeling and simulation in Python programming language. The design of PNet focuses on reducing the learning curve needed to define a Petri Net by using a text-based language rather than programming constructs to define transition rules. Complex transition rules can be refined as regular Python functions. To de...

PETRI NET MODELING OF COMPUTER VIRUS LIFE CYCLE

African Journals Online (AJOL)

Dr Obe

dynamic system analysis is applied to model the virus life cycle. Simulation of the derived model ... Keywords: Virus lifecycle, Petri nets, modeling. simulation. .... complex process. Figure 2 .... by creating Matlab files for five different computer ...

A bat's ear view of neural nets in physics

International Nuclear Information System (INIS)

Denby, B.

1997-01-01

The use of neural networks in high energy physics has become a field of its own which now has been in existence for ten years. This paper attempts to draw some conclusions on the utility of neural networks for physics applications, and also to make some projections for the future of this line of research. (orig.)

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.

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Proposal of a model of mammalian neural induction

Science.gov (United States)

Levine, Ariel J.; Brivanlou, Ali H.

2009-01-01

How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

QML-AiNet: An immune network approach to learning qualitative differential equation models.

Science.gov (United States)

Pang, Wei; Coghill, George M

2015-02-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

A quantum-implementable neural network model

Science.gov (United States)

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.

Optical neural network system for pose determination of spinning satellites

Science.gov (United States)

Lee, Andrew; Casasent, David

1990-01-01

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

Modeling of water treatment plant using timed continuous Petri nets

Science.gov (United States)

Nurul Fuady Adhalia, H.; Subiono, Adzkiya, Dieky

2017-08-01

Petri nets represent graphically certain conditions and rules. In this paper, we construct a model of the Water Treatment Plant (WTP) using timed continuous Petri nets. Specifically, we consider that (1) the water pump always active and (2) the water source is always available. After obtaining the model, the flow through the transitions and token conservation laws are calculated.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

Science.gov (United States)

Rolls, Edmund T

2012-01-01

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Neural estimation of kinetic rate constants from dynamic PET-scans

DEFF Research Database (Denmark)

Fog, Torben L.; Nielsen, Lars Hupfeldt; Hansen, Lars Kai

1994-01-01

A feedforward neural net is trained to invert a simple three compartment model describing the tracer kinetics involved in the metabolism of [18F]fluorodeoxyglucose in the human brain. The network can estimate rate constants from positron emission tomography sequences and is about 50 times faster ...

Fast And Flexible Modelling Of Real-Time Systems With Rtcp-Nets

Directory of Open Access Journals (Sweden)

Marcin Szpyrka

2004-01-01

Full Text Available A large number of formalisms has been proposed for real-time systems modelling. However, formal methods are not widely used in industrial software development. Such a situation could be treated as a result of a lack of suitable tools for fast designing of a model, its analysis and modification. RTCP-nets have been defined to facilitate fast modelling of embedded systems incorporating rule-based systems. Computer tools that are being developed for RTCP-nets, use a template mechanism to allow users to design models and manipulate its properties fast and effectively. Both theoretical and practical aspects of RTCP-nets are presented in the paper.

Fast and Flexible Modelling of Real-Time Systems with RTCP-Nets

Directory of Open Access Journals (Sweden)

Marcin Szpyrka

2004-01-01

Full Text Available A large number of formalisms has been proposed for real-time systems modelling. However, formal methods are not widely used in industrial software development. Such a situation could be treated as a result of a lack of suitable tools for fast designing of a model, its analysis and modification. RTCP-nets have been defined to facilitate fast modelling of embedded systems incorporating rule-based systems. Computer tools that are being developed for RTCP-nets, use a template mechanism to allow users to design models and manipulate its properties fast and effectively. Both theoretical and practical aspects of RTCP-nets are presented in the paper.

Functional model of biological neural networks.

Science.gov (United States)

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Neural Network for Image-to-Image Control of Optical Tweezers

Science.gov (United States)

Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

2004-01-01

A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

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.

MosquitoNet: investigating the use of UAV and artificial neural networks for integrated mosquito management

Science.gov (United States)

Case, E.; Ren, Y.; Shragai, T.; Erickson, D.

2017-12-01

Integrated mosquito control is expensive and resource intensive, and changing climatic factors are predicted to expand the habitable range of disease-carrying mosquitoes into new regions in the United States. Of particular concern in the northeastern United States are aedes albopictus, an aggressive, invasive species of mosquito that can transmit both native and exotic disease. Ae. albopictus prefer to live near human populations and breed in artificial containers with as little as two millimeters of standing water, exponentially increasing the difficulty of source control in suburban and urban areas. However, low-cost unmanned aerial vehicles (UAVs) can be used to photograph large regions at centimeter-resolution, and can image containers of interest in suburban neighborhoods. While proofs-of-concepts have been shown using UAVs to identify naturally occurring bodies of water, they have not been used to identify mosquito habitat in more populated areas. One of the primary challenges is that post-processing high-resolution aerial imagery is still time intensive, often labelled by hand or with programs built for satellite imagery. Artificial neural networks have been highly successful at image recognition tasks; in the past five years, convolutional neural networks (CNN) have surpassed or aided trained humans in identification of skin cancer, agricultural crops, and poverty levels from satellite imagery. MosquitoNet, a dual classifier built from the Single Shot Multibox Detector and VGG16 architectures, was trained on UAV­­­­­ aerial imagery taken during a larval study in Westchester County in southern New York State in July and August 2017. MosquitoNet was designed to assess the habitat risk of suburban properties by automating the identification and counting of containers like tires, toys, garbage bins, flower pots, etc. The SSD-based architecture marked small containers and other habitat indicators while the VGG16-based architecture classified the type of

Possibilistic Fuzzy Net Present Value Model and Application

Directory of Open Access Journals (Sweden)

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.

Modeling and control of magnetorheological fluid dampers using neural networks

Science.gov (United States)

Wang, D. H.; Liao, W. H.

2005-02-01

Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

Complex accident scenarios modelled and analysed by Stochastic Petri Nets

International Nuclear Information System (INIS)

Nývlt, Ondřej; Haugen, Stein; Ferkl, Lukáš

2015-01-01

This paper is focused on the usage of Petri nets for an effective modelling and simulation of complicated accident scenarios, where an order of events can vary and some events may occur anywhere in an event chain. These cases are hardly manageable by traditional methods as event trees – e.g. one pivotal event must be often inserted several times into one branch of the tree. Our approach is based on Stochastic Petri Nets with Predicates and Assertions and on an idea, which comes from the area of Programmable Logic Controllers: an accidental scenario is described as a net of interconnected blocks, which represent parts of the scenario. So the scenario is firstly divided into parts, which are then modelled by Petri nets. Every block can be easily interconnected with other blocks by input/output variables to create complex ones. In the presented approach, every event or a part of a scenario is modelled only once, independently on a number of its occurrences in the scenario. The final model is much more transparent then the corresponding event tree. The method is shown in two case studies, where the advanced one contains a dynamic behavior. - Highlights: • Event & Fault trees have problems with scenarios where an order of events can vary. • Paper presents a method for modelling and analysis of dynamic accident scenarios. • The presented method is based on Petri nets. • The proposed method solves mentioned problems of traditional approaches. • The method is shown in two case studies: simple and advanced (with dynamic behavior)

Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

African Journals Online (AJOL)

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 decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

Bio-Inspired Neural Model for Learning Dynamic Models

Science.gov (United States)

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.

Modeling biological gradient formation: combining partial differential equations and Petri nets.

Science.gov (United States)

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.

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...

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Coloured Petri Nets

DEFF Research Database (Denmark)

Jensen, Kurt

1987-01-01

The author describes a Petri net model, called coloured Petri nets (CP-nets), by means of which it is possible to describe large systems without having to cope with unnecessary details. The author introduces CP-nets and provide a first impression of their modeling power and the suitability...

A framework for quantifying net benefits of alternative prognostic models

OpenAIRE

Rapsomaniki, E.; White, I.R.; Wood, A.M.; Thompson, S.G.; Ford, I.

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 measure...

Neural networks in economic modelling : An empirical study

NARCIS (Netherlands)

Verkooijen, W.J.H.

1996-01-01

This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a

BioModels.net Web Services, a free and integrated toolkit for computational modelling software.

Science.gov (United States)

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.

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.

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

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.

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

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

Artificial neural network modelling

CERN Document Server

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. .

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....

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...

Modeling polyvinyl chloride Plasma Modification by Neural Networks

Science.gov (United States)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

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.

Identification of Complex Dynamical Systems with Neural Networks (2/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Identification of Complex Dynamical Systems with Neural Networks (1/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Open critical area model and extraction algorithm based on the net flow-axis

International Nuclear Information System (INIS)

Wang Le; Wang Jun-Ping; Gao Yan-Hong; Xu Dan; Li Bo-Bo; Liu Shi-Gang

2013-01-01

In the integrated circuit manufacturing process, the critical area extraction is a bottleneck to the layout optimization and the integrated circuit yield estimation. In this paper, we study the problem that the missing material defects may result in the open circuit fault. Combining the mathematical morphology theory, we present a new computation model and a novel extraction algorithm for the open critical area based on the net flow-axis. Firstly, we find the net flow-axis for different nets. Then, the net flow-edges based on the net flow-axis are obtained. Finally, we can extract the open critical area by the mathematical morphology. Compared with the existing methods, the nets need not to divide into the horizontal nets and the vertical nets, and the experimental results show that our model and algorithm can accurately extract the size of the open critical area and obtain the location information of the open circuit critical area. (interdisciplinary physics and related areas of science and technology)

Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets

Science.gov (United States)

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.

Development of infill drilling recovery models for carbonates reservoirs using neural networks and multivariate statistical as a novel method

International Nuclear Information System (INIS)

Soto, R; Wu, Ch. H; Bubela, A M

1999-01-01

This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in west Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), oilfield intelligence (01), as an engineering tool to improve the characterization of oil and gas reservoirs. 01 integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial water flooding, number of wells for primary recovery, number of infill wells over the initial water flooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery

Fuzzy-neural network in the automatic detection and volumetry of the spleen on spiral CT scans

International Nuclear Information System (INIS)

Heitmann, K.R.; Mainz Univ.; Rueckert, S.; Heussel, C.P.; Thelen, M.; Kauczor, H.U.; Uthmann, T.

2000-01-01

Purpose: To assess spleen segmentation and volumetry in spiral CT scans with and without pathological changes of splenic tissue. Methods: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. Results: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding>100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. Conclusions: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans. (orig.) [de

Perti Net-Based Workflow Access Control Model

Institute of Scientific and Technical Information of China (English)

陈卓; 骆婷; 石磊; 洪帆

2004-01-01

Access control is an important protection mechanism for information systems. This paper shows how to make access control in workflow system. We give a workflow access control model (WACM) based on several current access control models. The model supports roles assignment and dynamic authorization. The paper defines the workflow using Petri net. It firstly gives the definition and description of the workflow, and then analyzes the architecture of the workflow access control model (WACM). Finally, an example of an e-commerce workflow access control model is discussed in detail.

Artificial neural network model of pork meat cubes osmotic dehydration

OpenAIRE

Pezo, Lato L.; Ćurčić, Biljana Lj.; Filipović, Vladimir S.; Nićetin, Milica R.; Koprivica, Gordana B.; Mišljenović, Nevena M.; Lević, Ljubinko B.

2013-01-01

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 net...

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.

Data Sources for NetZero Ft Carson Model

Data.gov (United States)

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...

Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

Directory of Open Access Journals (Sweden)

Ding Fang

2013-06-01

Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

Feed forward neural networks modeling for K-P interactions

International Nuclear Information System (INIS)

El-Bakry, M.Y.

2003-01-01

Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

Neural network tagging in a toy model

International Nuclear Information System (INIS)

Milek, Marko; Patel, Popat

1999-01-01

The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

The equivalency between logic Petri workflow nets and workflow nets.

Science.gov (United States)

Wang, Jing; Yu, ShuXia; Du, YuYue

2015-01-01

Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in this paper is modeled to prove the equivalence between LPWNs and WF-nets, and advantages of LPWNs are presented.

The Equivalency between Logic Petri Workflow Nets and Workflow Nets

Science.gov (United States)

Wang, Jing; Yu, ShuXia; Du, YuYue

2015-01-01

Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in this paper is modeled to prove the equivalence between LPWNs and WF-nets, and advantages of LPWNs are presented. PMID:25821845

Software Design Modelling with Functional Petri Nets | Bakpo ...

African Journals Online (AJOL)

Software Design Modelling with Functional Petri Nets. ... of structured programs and a FPN Software prototype proposed for the conventional programming construct: if-then-else statement. ... EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

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. Petr...... with user-defined Standard ML functions. A license for CPN Tools can be obtained free of charge, also for commercial use....

Modelling collective cell migration of neural crest.

Science.gov (United States)

Szabó, András; Mayor, Roberto

2016-10-01

Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Petri Net-Based Software Process Model for Developing Process-Oriented Information Systems

Science.gov (United States)

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.

Evapotranspiration Modeling by Linear, Nonlinear Regression and Artificial Neural Network in Greenhouse (Case study Reference Crop, Cucumber and Tomato

Directory of Open Access Journals (Sweden)

vahid Rezaverdinejad

2017-01-01

Full Text Available Introduction: Greenhouse cultivation is a steadily developing agricultural sector throughout the world. In addition, it is known that water is a major issue almost all part of the world especially for countries which have insufficient water source. With this great expansion of greenhouse cultivation, the need of appropriate irrigation management has a great importance. Accurate determination of irrigation scheduling (irrigation timing and frequency is one of the main factors in achieving high yields and avoiding loss of quality in greenhouse tomato and cucumber. To do this, it is fundamental to know the crop water requirements or real evapotranspiration. Accurate estimation on crop water requirement is needed to avoid the excess or deficit water application, with consequent impacts on nutrient availability for plants. This can be done by using appropriate method to determine the crop evapotranspiration (ETc. In greenhouse cultivation, crop transpiration is the most important energy dissipation mechanisms that influence ETc rate. There are a large number of literatures on methods to estimate ETc in greenhouses. ETc can be measured or estimated by direct or indirect methods. The most common direct method estimates ETc from measurements with weighing lysimeters. Thisalsoincludes the evaporation measuring equipment, class A pan, Piche atmometer and modified atmometer. Indirect method includes the measurement of net radiation, temperature, relative humidity, and air vapour pressure deficit. A large number of models have been developed from these measurements to estimate ETc. Due to the fast development of under greenhouse cultivation all around the world, the needs of information on how it affects ETc in greenhouses has to be known and summarized. The existing models for ETc calculation have to be studied to know whether it is reliable for greenhouse climate (hereafter, microclimate or not. Regression and artificial neural network models are two

The CAFE model: A net production model for global ocean phytoplankton

Science.gov (United States)

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.

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.

Net-baryon-, net-proton-, and net-charge kurtosis in heavy-ion collisions within a relativistic transport approach

International Nuclear Information System (INIS)

Nahrgang, Marlene; Schuster, Tim; Stock, Reinhard; Mitrovski, Michael; Bleicher, Marcus

2012-01-01

We explore the potential of net-baryon, net-proton and net-charge kurtosis measurements to investigate the properties of hot and dense matter created in relativistic heavy-ion collisions. Contrary to calculations in a grand-canonical ensemble we explicitly take into account exact electric and baryon charge conservation on an event-by-event basis. This drastically limits the width of baryon fluctuations. A simple model to account for this is to assume a grand-canonical distribution with a sharp cut-off at the tails. We present baseline predictions of the energy dependence of the net-baryon, net-proton and net-charge kurtosis for central (b≤2.75 fm) Pb+Pb/Au+Au collisions from E lab =2A GeV to √(s NN )=200 GeV from the UrQMD model. While the net-charge kurtosis is compatible with values around zero, the net-baryon number decreases to large negative values with decreasing beam energy. The net-proton kurtosis becomes only slightly negative for low √(s NN ). (orig.)

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

Science.gov (United States)

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

Neural nets for massively parallel optimization

Science.gov (United States)

Dixon, Laurence C. W.; Mills, David

1992-07-01

To apply massively parallel processing systems to the solution of large scale optimization problems it is desirable to be able to evaluate any function f(z), z (epsilon) Rn in a parallel manner. The theorem of Cybenko, Hecht Nielsen, Hornik, Stinchcombe and White, and Funahasi shows that this can be achieved by a neural network with one hidden layer. In this paper we address the problem of the number of nodes required in the layer to achieve a given accuracy in the function and gradient values at all points within a given n dimensional interval. The type of activation function needed to obtain nonsingular Hessian matrices is described and a strategy for obtaining accurate minimal networks presented.

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

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.

Weather forecasting based on hybrid neural model

Science.gov (United States)

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.

Stability of a neural network model with small-world connections

International Nuclear Information System (INIS)

Li Chunguang; Chen Guanrong

2003-01-01

Small-world networks are highly clustered networks with small distances among the nodes. There are many biological neural networks that present this kind of connection. There are no special weightings in the connections of most existing small-world network models. However, this kind of simply connected model cannot characterize biological neural networks, in which there are different weights in synaptic connections. In this paper, we present a neural network model with weighted small-world connections and further investigate the stability of this model

Competition and Cooperation in Neural Nets : U.S.-Japan Joint Seminar

CERN Document Server

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 ...

Game Coloured Petri Nets

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...

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

International Nuclear Information System (INIS)

Soezen, Adnan; Arcaklioglu, Erol

2007-01-01

The most important theme in this study is to obtain equations based on economic indicators (gross national product-GNP and gross domestic product-GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968-2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R 2 values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections

Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

Science.gov (United States)

Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

2011-11-01

Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fine-grained vehicle type recognition based on deep convolution neural networks

Directory of Open Access Journals (Sweden)

Hongcai CHEN

2017-12-01

Full Text Available Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes. Aiming at the problems of great plice forces occupation, low retrieval efficiency, and lacking of intelligence for dealing with false license, fake plate vehicles and vehicles without plates, this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks. The filter size and numbers of convolution neural network are designed, the activation function and vehicle type classifier are optimally selected, and a new network framework is constructed for vehicle type fine-grained recognition. The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model. Moreover, the new model effectively reduces the number of training parameters, and saves computer memory. Fine-grained vehicle type recognition can be used in intelligent traffic management area, and has important theoretical research value and practical significance.

Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots

Directory of Open Access Journals (Sweden)

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

Transport energy demand modeling of South Korea using artificial neural network

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

Science.gov (United States)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

Science.gov (United States)

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.

Results from a MA16-based neural trigger in an experiment looking for beauty

International Nuclear Information System (INIS)

Baldanza, C.; Beichter, J.; Bisi, F.; Bruels, N.; Bruschini, C.; Cotta-Ramusino, A.; D'Antone, I.; Malferrari, L.; Mazzanti, P.; Musico, P.; Novelli, P.; Odorici, F.; Odorico, R.; Passaseo, M.; Zuffa, M.

1996-01-01

Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel's analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.)

Results from a MA16-based neural trigger in an experiment looking for beauty

Energy Technology Data Exchange (ETDEWEB)

Baldanza, C. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Beichter, J. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bisi, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Bruels, N. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bruschini, C. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Cotta-Ramusino, A. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); D`Antone, I. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Malferrari, L. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Mazzanti, P. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Musico, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Novelli, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Odorici, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Odorico, R. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Passaseo, M. [CERN, 1211 Geneva 23 (Switzerland); Zuffa, M. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy)

1996-07-11

Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel`s analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 {mu}s for a Fisher discriminant (1-layer net) and of 6 {mu}s for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.).

A Petri Nets Model for Blockchain Analysis

OpenAIRE

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...

Modelling human factor with Petri nets

International Nuclear Information System (INIS)

Bedreaga, Luminita; Constantinescu, Cristina; Guzun, Basarab

2007-01-01

The human contribution to risk and safety of nuclear power plant operation can be best understood, assessed and quantified using tools to evaluate human reliability. Human reliability analysis becomes an important part of every probabilistic safety assessment and it is used to demonstrate that nuclear power plants designed with different safety levels are prepared to cope with severe accidents. Human reliability analysis in context of probabilistic safety assessment consists in: identifying human-system interactions important to safety; quantifying probabilities appropriate with these interactions. Nowadays, the complex system functions can be modelled using special techniques centred either on states space adequate to system or on events appropriate to the system. Knowing that complex system model consists in evaluating the likelihood of success, in other words, in evaluating the possible value for that system being in some state, the inductive methods which are based on the system states can be applied also for human reliability modelling. Thus, switching to the system states taking into account the human interactions, the underlying basis of the Petri nets can be successfully applied and the likelihoods appropriate to these states can also derived. The paper presents the manner to assess the human reliability quantification using Petri nets approach. The example processed in the paper is from human reliability documentation without a detailed human factor analysis (qualitative). The obtained results by these two kinds of methods are in good agreement. (authors)

Numeral eddy current sensor modelling based on genetic neural network

International Nuclear Information System (INIS)

Yu Along

2008-01-01

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

Efficient airport detection using region-based fully convolutional neural networks

Science.gov (United States)

Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

2018-04-01

This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

Energy efficiency optimisation for distillation column using artificial neural network models

International Nuclear Information System (INIS)

Osuolale, Funmilayo N.; Zhang, Jie

2016-01-01

This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency. - Highlights: • Neural networks can accurately model exergy efficiency in distillation columns. • Bootstrap aggregated neural network offers improved model prediction accuracy. • Improved exergy efficiency is obtained through model based optimisation. • Reductions of utility consumption by 8.2% and 28.2% were achieved for binary systems. • The exergy efficiency for multi-component distillation is increased by 32.4%.

Gas Classification Using Deep Convolutional Neural Networks

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Gas Classification Using Deep Convolutional Neural Networks.

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

neural network based model o work based model of an industrial oil

African Journals Online (AJOL)

eobe

technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data

Science.gov (United States)

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.

Neural network modeling of emotion

Science.gov (United States)

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.

Associative memory model with spontaneous neural activity

Science.gov (United States)

Kurikawa, Tomoki; Kaneko, Kunihiko

2012-05-01

We propose a novel associative memory model wherein the neural activity without an input (i.e., spontaneous activity) is modified by an input to generate a target response that is memorized for recall upon the same input. Suitable design of synaptic connections enables the model to memorize input/output (I/O) mappings equaling 70% of the total number of neurons, where the evoked activity distinguishes a target pattern from others. Spontaneous neural activity without an input shows chaotic dynamics but keeps some similarity with evoked activities, as reported in recent experimental studies.

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.

Adaptive control using neural networks and approximate models.

Science.gov (United States)

Narendra, K S; Mukhopadhyay, S

1997-01-01

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

CERN Document Server

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.

Prediction model for initial point of net vapor generation for low-flow boiling

International Nuclear Information System (INIS)

Sun Qi; Zhao Hua; Yang Ruichang

2003-01-01

The prediction of the initial point of net vapor generation is significant for the calculation of phase distribution in sub-cooled boiling. However, most of the investigations were developed in high-flow boiling, and there is no common model that could be successfully applied for the low-flow boiling. A predictive model for the initial point of net vapor generation for low-flow forced convection and natural circulation is established here, by the analysis of evaporation and condensation heat transfer. The comparison between experimental data and calculated results shows that this model can predict the net vapor generation point successfully in low-flow sub-cooled boiling

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.

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

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Eiji Watanabe

2018-03-01

Full Text Available The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

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.

Biochemical Network Stochastic Simulator (BioNetS: software for stochastic modeling of biochemical networks

Directory of Open Access Journals (Sweden)

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.

Fluid Survival Tool: A Model Checker for Hybrid Petri Nets

NARCIS (Netherlands)

Postema, Björn Frits; Remke, Anne Katharina Ingrid; Haverkort, Boudewijn R.H.M.; Ghasemieh, Hamed

2014-01-01

Recently, algorithms for model checking Stochastic Time Logic (STL) on Hybrid Petri nets with a single general one-shot transition (HPNG) have been introduced. This paper presents a tool for model checking HPNG models against STL formulas. A graphical user interface (GUI) not only helps to

A framework for quantifying net benefits of alternative prognostic models.

Science.gov (United States)

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-30

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-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.

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.

A modified backpropagation algorithm for training neural networks on data with error bars

International Nuclear Information System (INIS)

Gernoth, K.A.; Clark, J.W.

1994-08-01

A method is proposed for training multilayer feedforward neural networks on data contaminated with noise. Specifically, we consider the case that the artificial neural system is required to learn a physical mapping when the available values of the target variable are subject to experimental uncertainties, but are characterized by error bars. The proposed method, based on maximum likelihood criterion for parameter estimation, involves simple modifications of the on-line backpropagation learning algorithm. These include incorporation of the error-bar assignments in a pattern-specific learning rate, together with epochal updating of a new measure of model accuracy that replaces the usual mean-square error. The extended backpropagation algorithm is successfully tested on two problems relevant to the modelling of atomic-mass systematics by neural networks. Provided the underlying mapping is reasonably smooth, neural nets trained with the new procedure are able to learn the true function to a good approximation even in the presence of high levels of Gaussian noise. (author). 26 refs, 2 figs, 5 tabs

Modeling of system reliability Petri nets with aging tokens

International Nuclear Information System (INIS)

Volovoi, V.

2004-01-01

The paper addresses the dynamic modeling of degrading and repairable complex systems. Emphasis is placed on the convenience of modeling for the end user, with special attention being paid to the modeling part of a problem, which is considered to be decoupled from the choice of solution algorithms. Depending on the nature of the problem, these solution algorithms can include discrete event simulation or numerical solution of the differential equations that govern underlying stochastic processes. Such modularity allows a focus on the needs of system reliability modeling and tailoring of the modeling formalism accordingly. To this end, several salient features are chosen from the multitude of existing extensions of Petri nets, and a new concept of aging tokens (tokens with memory) is introduced. The resulting framework provides for flexible and transparent graphical modeling with excellent representational power that is particularly suited for system reliability modeling with non-exponentially distributed firing times. The new framework is compared with existing Petri-net approaches and other system reliability modeling techniques such as reliability block diagrams and fault trees. The relative differences are emphasized and illustrated with several examples, including modeling of load sharing, imperfect repair of pooled items, multiphase missions, and damage-tolerant maintenance. Finally, a simple implementation of the framework using discrete event simulation is described

Classification of breast cancer cytological specimen using convolutional neural network

Science.gov (United States)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Finite element model updating of a small steel frame using neural networks

International Nuclear Information System (INIS)

Zapico, J L; González, M P; Alonso, R; González-Buelga, A

2008-01-01

This paper presents an experimental and analytical dynamic study of a small-scale steel frame. The experimental model was physically built and dynamically tested on a shaking table in a series of different configurations obtained from the original one by changing the mass and by causing structural damage. Finite element modelling and parameterization with physical meaning is iteratively tried for the original undamaged configuration. The finite element model is updated through a neural network, the natural frequencies of the model being the net input. The updating process is made more accurate and robust by using a regressive procedure, which constitutes an original contribution of this work. A novel simplified analytical model has been developed to evaluate the reduction of bending stiffness of the elements due to damage. The experimental results of the rest of the configurations have been used to validate both the updated finite element model and the analytical one. The statistical properties of the identified modal data are evaluated. From these, the statistical properties and a confidence interval for the estimated model parameters are obtained by using the Latin Hypercube sampling technique. The results obtained are successful: the updated model accurately reproduces the low modes identified experimentally for all configurations, and the statistical study of the transmission of errors yields a narrow confidence interval for all the identified parameters

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.......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...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

Pattern recognition neural-net by spatial mapping of biology visual field

Science.gov (United States)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

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.

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...... we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database...

Modeling of steam generator in nuclear power plant using neural network ensemble

International Nuclear Information System (INIS)

Lee, S. K.; Lee, E. C.; Jang, J. W.

2003-01-01

Neural network is now being used in modeling the steam generator is known to be difficult due to the reverse dynamics. However, Neural network is prone to the problem of overfitting. This paper investigates the use of neural network combining methods to model steam generator water level and compares with single neural network. The results show that neural network ensemble is effective tool which can offer improved generalization, lower dependence of the training set and reduced training time

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

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

A Quantum Implementation Model for Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Ammar Daskin

2018-02-01

Full Text Available The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase estimation algorithm is known to provide speedups over the conventional algorithms for the eigenvalue-related problems. Combining the quantum amplitude amplification with the phase estimation algorithm, a quantum implementation model for artificial neural networks using the Widrow–Hoff learning rule is presented. The complexity of the model is found to be linear in the size of the weight matrix. This provides a quadratic improvement over the classical algorithms. Quanta 2018; 7: 7–18.

Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

Science.gov (United States)

Tanadi, Theo

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

Time Extensions of Petri Nets for Modelling and Verification of Hard Real-Time Systems

Directory of Open Access Journals (Sweden)

Tomasz Szmuc

2002-01-01

Full Text Available The main aim ofthe paper is a presentation of time extensions of Petri nets appropriate for modelling and analysis of hard real-time systems. It is assumed, that the extensions must provide a model of time flow an ability to force a transition to fire within a stated timing constraint (the so-called the strong firing rule, and timing constraints represented by intervals. The presented survey includes extensions of classical Place/Transition Petri nets, as well as the ones applied to high-level Petri nets. An expressiveness of each time extension is illustrated using simple hard real-time system. The paper includes also a brief description of analysis and veryication methods related to the extensions, and a survey of software tools supporting modelling and analysis ofthe considered Petri nets.

Figure-Ground Organization Emerges in a Deep Net with a Feedback Loop

OpenAIRE

Zipser, Karl

2015-01-01

We used a deep net to model how object-specific activation at the high levels of a hierarchical neural network could be fed back to modify representations at lower levels. We first identified a subset of nodes in the uppermost hidden layer that were preferentially activated by images of people. We then ran a procedure to recursively modify an image so as to increase activation of the 'person-selective' nodes. The image was modified by choosing a rectangular region (of random size and position...

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Science.gov (United States)

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.

Modeling and Speed Control of Induction Motor Drives Using Neural Networks

Directory of Open Access Journals (Sweden)

V. Jamuna

2010-08-01

Full Text Available Speed control of induction motor drives using neural networks is presented. The mathematical model of single phase induction motor is developed. A new simulink model for a neural network-controlled bidirectional chopper fed single phase induction motor is proposed. Under normal operation, the true drive parameters are real-time identified and they are converted into the controller parameters through multilayer forward computation by neural networks. Comparative study has been made between the conventional and neural network controllers. It is observed that the neural network controlled drive system has better dynamic performance, reduced overshoot and faster transient response than the conventional controlled system.

Modeling and Simulation of Multi-scale Environmental Systems with Generalized Hybrid Petri Nets

Directory of Open Access Journals (Sweden)

Mostafa eHerajy

2015-07-01

Full Text Available Predicting and studying the dynamics and properties of environmental systems necessitates the construction and simulation of mathematical models entailing different levels of complexities. Such type of computational experiments often require the combination of discrete and continuous variables as well as processes operating at different time scales. Furthermore, the iterative steps of constructing and analyzing environmental models might involve researchers with different background. Hybrid Petri nets may contribute in overcoming such challenges as they facilitate the implementation of systems integrating discrete and continuous dynamics. Additionally, the visual depiction of model components will inevitably help to bridge the gap between scientists with distinct expertise working on the same problem. Thus, modeling environmental systems with hybrid Petri nets enables the construction of complex processes while keeping the models comprehensible for researchers working on the same project with significantly divergent education path. In this paper we propose the utilization of a special class of hybrid Petri nets, Generalized Hybrid Petri Nets (GHPN, to model and simulate environmental systems exposing processes interacting at different time-scales. GHPN integrate stochastic and deterministic semantics as well as other types of special basic events. Moreover, a case study is presented to illustrate the use of GHPN in constructing and simulating multi-timescale environmental scenarios.

A model of interval timing by neural integration.

Science.gov (United States)

Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

2011-06-22

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.

A framework for quantifying net benefits of alternative prognostic models

NARCIS (Netherlands)

Rapsomaniki, E.; White, I.R.; Wood, A.M.; Thompson, S.G.; Feskens, E.J.M.; Kromhout, D.

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)

Higher-moment measurements of net-kaon, net-charge and net-proton multiplicity distributions at STAR

International Nuclear Information System (INIS)

Sarkar, Amal

2014-01-01

In this paper, we report the measurements of the various moments, such as mean, standard deviation (σ), skewness (S) and kurtosis (κ) of the net-kaon, net-charge and net-proton multiplicity distributions at mid-rapidity in Au + Au collisions from √(s NN )=7.7 to 200 GeV with the STAR experiment at RHIC. This work has been done with the aim to locate the critical point on the QCD phase diagram. These moments and their products are related to the thermodynamic susceptibilities of conserved quantities such as net baryon number, net charge, and net strangeness as well as to the correlation length of the system which diverges in an ideal infinite thermodynamic system at the critical point. For a finite system, existing for a finite time, a non-monotonic behavior of these variables would indicate the presence of the critical point. Furthermore, we also present the moment products Sσ, κσ 2 of net-kaon, net-charge and net-proton multiplicity distributions as a function of collision centrality and energy. The energy and the centrality dependence of higher moments and their products have been compared with different models

Threat driven modeling framework using petri nets for e-learning system.

Science.gov (United States)

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.

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

Science.gov (United States)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Net energy analysis in a Ramsey–Hotelling growth model

International Nuclear Information System (INIS)

Macías, Arturo; Matilla-García, Mariano

2015-01-01

This article presents a dynamic growth model with energy as an input in the production function. The available stock of energy resources is ordered by a quality parameter based on energy accounting: the “Energy Return on Energy Invested” (EROI). In our knowledge this is the first paper where EROI fits in a neoclassical growth model (with individual utility maximization and market equilibrium), establishing the economic use of “net energy analysis” on a firmer theoretical ground. All necessary concepts to link neoclassical economics and EROI are discussed before their use in the model, and a comparative static analysis of the steady states of a simplified version of the model is presented. - Highlights: • A neoclassical growth model with EROI (“Energy Return on Energy Invested”) is shown • All concepts linking neoclassical economics and net energy analysis are discussed • Any EROI decline can be compensated increasing gross activity in the energy sector. • The economic impact of EROI depends on some non-energy cost in the energy sector. • Comparative steady-state statics for different EROI levels is performed and discussed. • Policy implications are suggested.

Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

Directory of Open Access Journals (Sweden)

Ayman Hamdy Kassem

2011-01-01

Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

[The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

Science.gov (United States)

Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

2016-03-01

The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

International Nuclear Information System (INIS)

Makaveev, Dimitre; Dupre, Luc; De Wulf, Marc; Melkebeek, Jan

2001-01-01

A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling. [copyright] 2001 American Institute of Physics

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

Science.gov (United States)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Knowledge base and neural network approach for protein secondary structure prediction.

Science.gov (United States)

Patel, Maulika S; Mazumdar, Himanshu S

2014-11-21

Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

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-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

African Journals Online (AJOL)

A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

Cognon Neural Model Software Verification and Hardware Implementation Design

Science.gov (United States)

Haro Negre, Pau

Little is known yet about how the brain can recognize arbitrary sensory patterns within milliseconds using neural spikes to communicate information between neurons. In a typical brain there are several layers of neurons, with each neuron axon connecting to ˜104 synapses of neurons in an adjacent layer. The information necessary for cognition is contained in theses synapses, which strengthen during the learning phase in response to newly presented spike patterns. Continuing on the model proposed in "Models for Neural Spike Computation and Cognition" by David H. Staelin and Carl H. Staelin, this study seeks to understand cognition from an information theoretic perspective and develop potential models for artificial implementation of cognition based on neuronal models. To do so we focus on the mathematical properties and limitations of spike-based cognition consistent with existing neurological observations. We validate the cognon model through software simulation and develop concepts for an optical hardware implementation of a network of artificial neural cognons.

StarNet: An application of deep learning in the analysis of stellar spectra

Science.gov (United States)

Kielty, Collin; Bialek, Spencer; Fabbro, Sebastien; Venn, Kim; O'Briain, Teaghan; Jahandar, Farbod; Monty, Stephanie

2018-06-01

In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact. These surveys provide a homogeneous database of stellar spectra that are ideal for machine learning applications. In this poster, we present StarNet: a convolutional neural network model applied to the analysis of both SDSS-III APOGEE DR13 and synthetic stellar spectra. When trained on synthetic spectra alone, the calculated stellar parameters (temperature, surface gravity, and metallicity) are of excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. While StarNet was developed using the APOGEE observed spectra and corresponding ASSeT synthetic grid, we suggest that this technique is applicable to other spectral resolutions, spectral surveys, and wavelength regimes. As a demonstration of this, we present a StarNet model trained on lower resolution, R=6000, IR synthetic spectra, describing the spectra delivered by Gemini/NIFS and the forthcoming Gemini/GIRMOS instrument (PI Sivanandam, UToronto). Preliminary results suggest that the stellar parameters determined from this low resolution StarNet model are comparable in precision to the high-resolution APOGEE results. The success of StarNet at lower resolution can be attributed to (1) a large training set of synthetic spectra (N ~200,000) with a priori stellar labels, and (2) the use of the entire spectrum in the solution rather than a few weighted windows, which are common methods in other spectral analysis tools (e.g. FERRE or The Cannon). Remaining challenges in our StarNet applications include rectification, continuum normalization, and wavelength coverage. Solutions to these problems could be used to guide decisions made in the development of future spectrographs, spectroscopic surveys, and data reduction pipelines, such as for the future MSE.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

Net Rotation of the Lithosphere in Mantle Convection Models with Self-consistent Plate Generation

Science.gov (United States)

Gerault, M.; Coltice, N.

2017-12-01

Lateral variations in the viscosity structure of the lithosphere and the mantle give rise to a discordant motion between the two. In a deep mantle reference frame, this motion is called the net rotation of the lithosphere. Plate motion reconstructions, mantle flow computations, and inferences from seismic anisotropy all indicate some amount of net rotation using different mantle reference frames. While the direction of rotation is somewhat consistent across studies, the predicted amplitudes range from 0.1 deg/Myr to 0.3 deg/Myr at the present-day. How net rotation rates could have differed in the past is also a subject of debate and strong geodynamic arguments are missing from the discussion. This study provides the first net rotation calculations in 3-D spherical mantle convection models with self-consistent plate generation. We run the computations for billions of years of numerical integration. We look into how sensitive the net rotation is to major tectonic events, such as subduction initiation, continental breakup and plate reorganisations, and whether some governing principles from the models could guide plate motion reconstructions. The mantle convection problem is solved with the finite volume code StagYY using a visco-pseudo-plastic rheology. Mantle flow velocities are solely driven by buoyancy forces internal to the system, with free slip upper and lower boundary conditions. We investigate how the yield stress, the mantle viscosity structure and the properties of continents affect the net rotation over time. Models with large lateral viscosity variations from continents predict net rotations that are at least threefold faster than those without continents. Models where continents cover a third of the surface produce net rotation rates that vary from nearly zero to over 0.3 deg/Myr with rapide increase during continental breakup. The pole of rotation appears to migrate along no particular path. For all models, regardless of the yield stress and the

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Temporal-pattern learning in neural models

CERN Document Server

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...

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

Science.gov (United States)

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.

A neural network model for credit risk evaluation.

Science.gov (United States)

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Connectionist Modelling and Education.

Science.gov (United States)

Evers, Colin W.

2000-01-01

Provides a detailed, technical introduction to the state of cognitive science research, in particular the rise of the "new cognitive science," especially artificial neural net (ANN) models. Explains one influential ANN model and describes diverse applications and their implications for education. (EV)

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

International Nuclear Information System (INIS)

Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy

2016-01-01

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

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.

A case study to estimate costs using Neural Networks and regression based models

Directory of Open Access Journals (Sweden)

Nadia Bhuiyan

2012-07-01

Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

A continuous-time neural model for sequential action.

Science.gov (United States)

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.

CSL model checking of deterministic and stochastic Petri nets

NARCIS (Netherlands)

Martinez Verdugo, J.M.; Haverkort, Boudewijn R.H.M.; German, R.; Heindl, A.

2006-01-01

Deterministic and Stochastic Petri Nets (DSPNs) are a widely used high-level formalism for modeling discrete-event systems where events may occur either without consuming time, after a deterministic time, or after an exponentially distributed time. The underlying process dened by DSPNs, under

A neutron spectrum unfolding computer code based on artificial neural networks

International Nuclear Information System (INIS)

Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

2014-01-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, 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 Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(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, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

Modelling a variable valve timing spark ignition engine using different neural networks

Energy Technology Data Exchange (ETDEWEB)

Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group

2004-10-01

In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)

Unloading arm movement modeling using neural networks for a rotary hearth furnace

Directory of Open Access Journals (Sweden)

Iulia Inoan

2011-12-01

Full Text Available Neural networks are being applied in many fields of engineering having nowadays a wide range of application. Neural networks are very useful for modeling dynamic processes for which the mathematical modeling is hard to obtain, or for processes that can’t be modeled using mathematical equations. This paper describes the modeling process for the unloading arm movement from a rotary hearth furnace using neural networks with back propagation algorithm. In this case the designed network was trained using the simulation results from a previous calculated mathematical model.

Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

International Nuclear Information System (INIS)

Willoughby, Twyla R.; Starkschall, George; Janjan, Nora A.; Rosen, Isaac I.

1996-01-01

Purpose: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Methods and Materials: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Results: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. Conclusions: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit

Neural nets for the plausibility check of measured values in the integrated measurement and information system for the surveillance of environmental radioactivity (IMIS)

International Nuclear Information System (INIS)

Haase, G.

2003-01-01

Neural nets to the plausibility check of measured values in the ''integrated measurement and information system for the surveillance of environmental radioactivity, IMIS'' is a research project supported by the Federal Minister for the Environment, Nature Conservation and Nuclear Safety. A goal of this project was the automatic recognition of implausible measured values in the data base ORACLE, which measured values from surveillance of environmental radioactivity of most diverse environmental media contained. The conversion of this project [ 1 ] was realized by institut of logic, complexity and deduction systems of the university Karlsruhe under the direction of Professor Dr. Menzel, Dr. Martin Riedmueller and Martin Lauer. (orig.)

Reconstruction of neutron spectra through neural networks; Reconstruccion de espectros de neutrones mediante redes neuronales

Energy Technology Data Exchange (ETDEWEB)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E. [Cuerpo Academico de Radiobiologia, Estudios Nucleares, Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico)] e-mail: rvega@cantera.reduaz.mx [and others

2003-07-01

A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

Neural Networks for Modeling and Control of Particle Accelerators

Science.gov (United States)

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.

NET model coil test possibilities in the TOSKA TWIN configuration

International Nuclear Information System (INIS)

Gruenhagen, A.; Heller, R.; Herz, W.; Hofmann, A.; Jentzsch, K.; Kapulla, H.; Kneifel, B.; Komarek, P.; Lehmann, W.; Maurer, W.; Ulbricht, A.; Vogt, A.; Zahn, G.

1989-07-01

This report continues an earlier one on the possibilities of NET model coil testing in the TOSKA Upgrade facility at KfK. The investigation of a 'Cluster Test Facility' and a 'Solenoid Test Facility' is followed by the investigation of two further test arrangements. They are called 'Twin Configurations'. One common feature of both arrangements is that the EURATOM-LCT-coil delivers a background magnetic field. This coil should be operated at a temperature of 1.8 K and an enhanced current up to 20 kA compared to the LCT test where 3.5 K and up to 16 kA were the operating conditions. In one configuration the NET model test coil is adjacent to the LCT coil (ATC = Adjacent Twin Configuration), in the other one the NET model coil is inserted into the bore of LCT coil (ITC = Inserted Twin Configuration) either upright or with a 60 0 C slope. The configurations are investigated with respect to their electromagnetic mechanical and thermo-hydraulic properties. The requirements for the necessary mechanical support structure of the LCT coil were computed. Installation and cooling of the whole system were discussed. The time schedule and the costs for the test facility modification were estimated. Advantages and disadvantages for the configurations were discussed with respect to feasibility of the test arrangement and operation. (orig.) [de

Modelling the permeability of polymers: a neural network approach

NARCIS (Netherlands)

Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.

1994-01-01

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a

Daily rainfall-runoff modelling by neural networks in semi-arid zone ...

African Journals Online (AJOL)

This research work will allow checking efficiency of formal neural networks for flows' modelling of wadi Ouahrane's basin from rainfall-runoff relation which is non-linear. Two models of neural networks were optimized through supervised learning and compared in order to achieve this goal, the first model with input rain, and ...

Health safety nets can break cycles of poverty and disease: a stochastic ecological model.

Science.gov (United States)

Plucinski, Mateusz M; Ngonghala, Calistus N; Bonds, Matthew H

2011-12-07

The persistence of extreme poverty is increasingly attributed to dynamic interactions between biophysical processes and economics, though there remains a dearth of integrated theoretical frameworks that can inform policy. Here, we present a stochastic model of disease-driven poverty traps. Whereas deterministic models can result in poverty traps that can only be broken by substantial external changes to the initial conditions, in the stochastic model there is always some probability that a population will leave or enter a poverty trap. We show that a 'safety net', defined as an externally enforced minimum level of health or economic conditions, can guarantee ultimate escape from a poverty trap, even if the safety net is set within the basin of attraction of the poverty trap, and even if the safety net is only in the form of a public health measure. Whereas the deterministic model implies that small improvements in initial conditions near the poverty-trap equilibrium are futile, the stochastic model suggests that the impact of changes in the location of the safety net on the rate of development may be strongest near the poverty-trap equilibrium.

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation.

Science.gov (United States)

Sherfey, Jason S; Soplata, Austin E; Ardid, Salva; Roberts, Erik A; Stanley, David A; Pittman-Polletta, Benjamin R; Kopell, Nancy J

2018-01-01

DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.

Application of artificial neural nets to Shashlik calorimetry

International Nuclear Information System (INIS)

Bonesini, M.; Paganoni, M.; Terranova, F.

1997-01-01

Artificial neural networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given. (orig.)

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

Science.gov (United States)

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.

Statistical mechanics of attractor neural network models with synaptic depression

International Nuclear Information System (INIS)

Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

2009-01-01

Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

Science.gov (United States)

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

APPLYING PETRI NETS EXTENSIONS TO MODELING COMMERCIAL BANK ACTIVITY

Directory of Open Access Journals (Sweden)

Igor ENICOV

2017-02-01

Full Text Available The relevance of the study is determined by the need to improve the methods of modeling andsimulating commercial bank activity, including for the purpose of calculating, controlling and managingthe risk of the bank, in the context of the transition to the application of Basel III standards. Thisimprovement becomes necessary due to a direct transition to new regulatory standards when the internalassessments of the main risks become the initial data for calculating the capital adequacy of a bank. Thepurpose of this article is to argue the opportunity to formulate a theory of the commercial bank model onthe extensions of Petri nets theory. The main methods of research were the method of scientific abstractionand method of logical analysis. The main result obtained in the study and presented in the article is theargumentation of the possibility to analyze the quantitative and qualitative characteristics of acommercial bank with the help of Petri net extensions.

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

Science.gov (United States)

Zelić, B; Bolf, N; Vasić-Racki, D

2006-06-01

Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

Institute of Scientific and Technical Information of China (English)

QINGMing; CAOYue; HUANGTian-min

2004-01-01

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.

Cost and cost effectiveness of long-lasting insecticide-treated bed nets - a model-based analysis

Directory of Open Access Journals (Sweden)

Pulkki-Brännström Anni-Maria

2012-04-01

Full Text Available Abstract Background The World Health Organization recommends that national malaria programmes universally distribute long-lasting insecticide-treated bed nets (LLINs. LLINs provide effective insecticide protection for at least three years while conventional nets must be retreated every 6-12 months. LLINs may also promise longer physical durability (lifespan, but at a higher unit price. No prospective data currently available is sufficient to calculate the comparative cost effectiveness of different net types. We thus constructed a model to explore the cost effectiveness of LLINs, asking how a longer lifespan affects the relative cost effectiveness of nets, and if, when and why LLINs might be preferred to conventional insecticide-treated nets. An innovation of our model is that we also considered the replenishment need i.e. loss of nets over time. Methods We modelled the choice of net over a 10-year period to facilitate the comparison of nets with different lifespan (and/or price and replenishment need over time. Our base case represents a large-scale programme which achieves high coverage and usage throughout the population by distributing either LLINs or conventional nets through existing health services, and retreats a large proportion of conventional nets regularly at low cost. We identified the determinants of bed net programme cost effectiveness and parameter values for usage rate, delivery and retreatment cost from the literature. One-way sensitivity analysis was conducted to explicitly compare the differential effect of changing parameters such as price, lifespan, usage and replenishment need. Results If conventional and long-lasting bed nets have the same physical lifespan (3 years, LLINs are more cost effective unless they are priced at more than USD 1.5 above the price of conventional nets. Because a longer lifespan brings delivery cost savings, each one year increase in lifespan can be accompanied by a USD 1 or more increase in price

Evaluation of Cyber Security and Modelling of Risk Propagation with Petri Nets

Directory of Open Access Journals (Sweden)

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.

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

Science.gov (United States)

Li, A. S. X.; Chirayath, V.; Segal-Rosenhaimer, M.; Das, K.

2017-12-01

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% 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 Python's extensive libraries for machine learning, as well as extending integration to GPU

Petri Net-Based R&D Process Modeling and Optimization for Composite Materials

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2013-01-01

Full Text Available Considering the current R&D process for new composite materials involves some complex details, such as formula design, specimen/sample production, materials/sample test, assessment, materials/sample feedback from customers, and mass production, the workflow model of Petri net-based R&D process for new composite materials’ is proposed. By analyzing the time property of the whole Petri net, the optimized model for new composite materials R&D workflow is further proposed. By analyzing the experiment data and application in some materials R&D enterprise, it is demonstrated that the workflow optimization model shortens the period of R&D on new materials for 15%, definitely improving the R&D efficiency. This indicates the feasibility and availability of the model.

QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks

Science.gov (United States)

Minaudo, Camille; Curie, Florence; Jullian, Yann; Gassama, Nathalie; Moatar, Florentina

2018-04-01

To allow climate change impact assessment of water quality in river systems, the scientific community lacks efficient deterministic models able to simulate hydrological and biogeochemical processes in drainage networks at the regional scale, with high temporal resolution and water temperature explicitly determined. The model QUALity-NETwork (QUAL-NET) was developed and tested on the Middle Loire River Corridor, a sub-catchment of the Loire River in France, prone to eutrophication. Hourly variations computed efficiently by the model helped disentangle the complex interactions existing between hydrological and biological processes across different timescales. Phosphorus (P) availability was the most constraining factor for phytoplankton development in the Loire River, but simulating bacterial dynamics in QUAL-NET surprisingly evidenced large amounts of organic matter recycled within the water column through the microbial loop, which delivered significant fluxes of available P and enhanced phytoplankton growth. This explained why severe blooms still occur in the Loire River despite large P input reductions since 1990. QUAL-NET could be used to study past evolutions or predict future trajectories under climate change and land use scenarios.

QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks

Directory of Open Access Journals (Sweden)

C. Minaudo

2018-04-01

Full Text Available To allow climate change impact assessment of water quality in river systems, the scientific community lacks efficient deterministic models able to simulate hydrological and biogeochemical processes in drainage networks at the regional scale, with high temporal resolution and water temperature explicitly determined. The model QUALity-NETwork (QUAL-NET was developed and tested on the Middle Loire River Corridor, a sub-catchment of the Loire River in France, prone to eutrophication. Hourly variations computed efficiently by the model helped disentangle the complex interactions existing between hydrological and biological processes across different timescales. Phosphorus (P availability was the most constraining factor for phytoplankton development in the Loire River, but simulating bacterial dynamics in QUAL-NET surprisingly evidenced large amounts of organic matter recycled within the water column through the microbial loop, which delivered significant fluxes of available P and enhanced phytoplankton growth. This explained why severe blooms still occur in the Loire River despite large P input reductions since 1990. QUAL-NET could be used to study past evolutions or predict future trajectories under climate change and land use scenarios.

Neural net classification of x-ray pistachio nut data

Science.gov (United States)

Casasent, David P.; Sipe, Michael A.; Schatzki, Thomas F.; Keagy, Pamela M.; Le, Lan Chau

1996-12-01

Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.

Fuzzy Neuroidal Nets and Recurrent Fuzzy Computations

Czech Academy of Sciences Publication Activity Database

Wiedermann, Jiří

2001-01-01

Roč. 11, č. 6 (2001), s. 675-686 ISSN 1210-0552. [SOFSEM 2001 Workshop on Soft Computing. Piešťany, 29.11.2001-30.11.2001] R&D Projects: GA ČR GA201/00/1489; GA AV ČR KSK1019101 Institutional research plan: AV0Z1030915 Keywords : fuzzy computing * fuzzy neural nets * fuzzy Turing machines * non-uniform computational complexity Subject RIV: BA - General Mathematics

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

Science.gov (United States)

Hsu, Ken-Yuh (Editor); Liu, Hua-Kuang (Editor)

1992-01-01

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

Science.gov (United States)

Hsu, Ken-Yuh; Liu, Hua-Kuang

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Application of Convolution Neural Network to the forecasts of flare classification and occurrence using SOHO MDI data

Science.gov (United States)

Park, Eunsu; Moon, Yong-Jae

2017-08-01

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.86 for flare classification and 0.84 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

Science.gov (United States)

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.

A Pruning Neural Network Model in Credit Classification Analysis

Directory of Open Access Journals (Sweden)

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.

Modeling of surface dust concentrations using neural networks and kriging

Science.gov (United States)

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.

Models of neural dynamics in brain information processing - the developments of 'the decade'

International Nuclear Information System (INIS)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B; Ivanitskii, Genrikh R

2002-01-01

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

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.

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

Science.gov (United States)

Fabijańska, Anna

2018-04-18

Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network. Particularly, the network is trained to discriminate pixels located at the borders between cells. The edge probability map outputted by the network is next binarized and skeletonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared edges. The corresponding mean absolute percentage error of cell number is at the level of 4.5% which confirms the high accuracy of the proposed approach. The resulting cell edges are well aligned to the ground truths and require a limited number of manual corrections. This also results in accurate values of the cell morphometric parameters. The corresponding errors range from 5.2% for endothelial cell density, through 6.2% for cell hexagonality to 11.93% for the coefficient of variation of the cell size. Copyright © 2018 Elsevier B.V. All rights reserved.

High-level Petri Nets

DEFF Research Database (Denmark)

various journals and collections. As a result, much of this knowledge is not readily available to people who may be interested in using high-level nets. Within the Petri net community this problem has been discussed many times, and as an outcome this book has been compiled. The book contains reprints...... of some of the most important papers on the application and theory of high-level Petri nets. In this way it makes the relevant literature more available. It is our hope that the book will be a useful source of information and that, e.g., it can be used in the organization of Petri net courses. To make......High-level Petri nets are now widely used in both theoretical analysis and practical modelling of concurrent systems. The main reason for the success of this class of net models is that they make it possible to obtain much more succinct and manageable descriptions than can be obtained by means...

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.

Global reinforcement training of CrossNets

Science.gov (United States)

Ma, Xiaolong

2007-10-01

Hybrid "CMOL" integrated circuits, incorporating advanced CMOS devices for neural cell bodies, nanowires as axons and dendrites, and latching switches as synapses, may be used for the hardware implementation of extremely dense (107 cells and 1012 synapses per cm2) neuromorphic networks, operating up to 10 6 times faster than their biological prototypes. We are exploring several "Cross- Net" architectures that accommodate the limitations imposed by CMOL hardware and should allow effective training of the networks without a direct external access to individual synapses. Our studies have show that CrossNets based on simple (two-terminal) crosspoint devices can work well in at least two modes: as Hop-field networks for associative memory and multilayer perceptrons for classification tasks. For more intelligent tasks (such as robot motion control or complex games), which do not have "examples" for supervised learning, more advanced training methods such as the global reinforcement learning are necessary. For application of global reinforcement training algorithms to CrossNets, we have extended Williams's REINFORCE learning principle to a more general framework and derived several learning rules that are more suitable for CrossNet hardware implementation. The results of numerical experiments have shown that these new learning rules can work well for both classification tasks and reinforcement tasks such as the cartpole balancing control problem. Some limitations imposed by the CMOL hardware need to be carefully addressed for the the successful application of in situ reinforcement training to CrossNets.

A modeling method of semiconductor fabrication flows with extended knowledge hybrid Petri nets

Institute of Scientific and Technical Information of China (English)

Zhou Binghai; Jiang Shuyu; Wang Shijin; Wu bin

2008-01-01

A modeling method of extended knowledge hybrid Petri nets (EKHPNs), incorporating object-oriented methods into hybrid Petri nets (HPNs), was presented and used for the representation and modeling of semiconductor wafer fabrication flows. To model the discrete and continuous parts of a complex semiconductor wafer fabrication flow, the HPNs were introduced into the EKHPNs. Object-oriented methods were combined into the EKHPNs for coping with the complexity of the fabrication flow. Knowledge annotations were introduced to solve input and output conflicts of the EKHPNs.Finally, to demonstrate the validity of the EKHPN method, a real semiconductor wafer fabrication case was used to illustrate the modeling procedure. The modeling results indicate that the proposed method can be used to model a complex semiconductor wafer fabrication flow expediently.

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Ammar Daskin

2018-01-01

The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the pha...

VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography.

Science.gov (United States)

Huang, Ruobing; Xie, Weidi; Alison Noble, J

2018-04-23

Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perform and even then is tedious. Inspired by how sonographers view and interact with volumes during real-time clinical scanning, we propose an efficient automatic method to simultaneously localize multiple brain structures in 3D fetal neurosonography. The proposed View-based Projection Networks (VP-Nets), uses three view-based Convolutional Neural Networks (CNNs), to simplify 3D localizations by directly predicting 2D projections of the key structures onto three anatomical views. While designed for efficient use of data and GPU memory, the proposed VP-Nets allows for full-resolution 3D prediction. We investigated parameters that influence the performance of VP-Nets, e.g. depth and number of feature channels. Moreover, we demonstrate that the model can pinpoint the structure in 3D space by visualizing the trained VP-Nets, despite only 2D supervision being provided for a single stream during training. For comparison, we implemented two other baseline solutions based on Random Forest and 3D U-Nets. In the reported experiments, VP-Nets consistently outperformed other methods on localization. To test the importance of loss function, two identical models are trained with binary corss-entropy and dice coefficient loss respectively. Our best VP-Net model achieved prediction center deviation: 1.8 ± 1.4 mm, size difference: 1.9 ± 1.5 mm, and 3D Intersection Over Union (IOU): 63.2 ± 14.7% when compared to the ground truth. To make the whole pipeline intervention free, we also implement a skull-stripping tool using 3D CNN, which achieves high segmentation accuracy. As a result, the proposed processing pipeline takes a raw ultrasound brain image as input, and output a skull-stripped image with five detected key brain

Portable Rule Extraction Method for Neural Network Decisions Reasoning

Directory of Open Access Journals (Sweden)

Darius PLIKYNAS

2005-08-01

Full Text Available Neural network (NN methods are sometimes useless in practical applications, because they are not properly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN decisions explanatory power. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophistication of the rule extraction technique etc. The authors propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool in the financial problems' domain and SOM (input data space clusterization. The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

BP fusion model for the detection of oil spills on the sea by remote sensing

Science.gov (United States)

Chen, Weiwei; An, Jubai; Zhang, Hande; Lin, Bin

2003-06-01

Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills" image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators" result of edge gray level and the image"s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image"s edge detection.

Neural Network Approach to Locating Cryptography in Object Code

Energy Technology Data Exchange (ETDEWEB)

Jason L. Wright; Milos Manic

2009-09-01

Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

Directory of Open Access Journals (Sweden)

F. Bagheri

2013-02-01

Full Text Available The Electrocardiogram (ECG signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology

International Nuclear Information System (INIS)

Knezevic, J.; Odoom, E.R.

2001-01-01

A methodology is developed which uses Petri nets instead of the fault tree methodology and solves for reliability indices utilising fuzzy Lambda-Tau method. Fuzzy set theory is used for representing the failure rate and repair time instead of the classical (crisp) set theory because fuzzy numbers allow expert opinions, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into the system model. Petri nets are used because unlike the fault tree methodology, the use of Petri nets allows efficient simultaneous generation of minimal cut and path sets

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

Science.gov (United States)

Mezgec, Simon; Koroušić Seljak, Barbara

2017-06-27

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

Robust modelling and simulation integration of SIMIO with coloured petri nets

CERN Document Server

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...

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)

Model-Based Testing of a Reactive System with Coloured Petri Nets

DEFF Research Database (Denmark)

Tjell, Simon

2006-01-01

In this paper, a reactive and nondeterministic system is tested. This is doneby applying a generic model that has been specified as a configurable Coloured PetriNet. In this way, model-based testing is possible for a wide class of reactive system atthe level of discrete events. Concurrently...

MODELLING OF CONCENTRATION LIMITS BASED ON NEURAL NETWORKS.

Directory of Open Access Journals (Sweden)

A. L. Osipov

2017-02-01

Full Text Available We study the forecasting model with the concentration limits is-the use of neural network technology. The software for the implementation of these models. It is shown that the efficiency of the system in the experimental material.

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

Science.gov (United States)

Crago, Patrick E; Makowski, Nathaniel S

2014-10-01

Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

Artificial Neural Network L* from different magnetospheric field models

Science.gov (United States)

Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

2011-12-01

The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

Applying Petri nets in modelling the human factor

International Nuclear Information System (INIS)

Bedreaga, Luminita; Constntinescu, Cristina; Guzun, Basarab

2007-01-01

Usually, in the reliability analysis performed for complex systems, we determine the success probability to work with other performance indices, i.e. the likelihood associated with a given state. The possible values assigned to system states can be derived using inductive methods. If one wants to calculate the probability to occur a particular event in the system, then deductive methods should be applied. In the particular case of the human reliability analysis, as part of probabilistic safety analysis, the international regulatory commission have developed specific guides and procedures to perform such assessments. The paper presents the modality to obtain the human reliability quantification using the Petri nets approach. This is an efficient means to assess reliability systems because of their specific features. The examples showed in the paper are from human reliability documentation without a detailed human factor analysis (qualitative). We present human action modelling using event trees and Petri nets approach. The obtained results by these two kinds of methods are in good concordance. (authors)

Embedding recurrent neural networks into predator-prey models.

Science.gov (United States)

Moreau, Yves; Louiès, Stephane; Vandewalle, Joos; Brenig, Leon

1999-03-01

We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Daskin, Ammar

2016-01-01

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase...

Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

International Nuclear Information System (INIS)

Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

2009-01-01

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

Science.gov (United States)

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Parametric models to relate spike train and LFP dynamics with neural information processing.

Science.gov (United States)

Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

2012-01-01

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial

Coloured Petri Nets

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...

A neural network model of the relativistic electron flux at geosynchronous orbit

International Nuclear Information System (INIS)

Koons, H.C.; Gorney, D.J.

1991-01-01

A neural network has been developed to model the temporal variations of relativistic (>3 MeV) electrons at geosynchronous orbit based on model inputs consisting of 10 consecutive days of the daily sum of the planetary magnetic index ΣKp. The neural network consists of three layers of neurons, containing 10 neurons in the input layer, 6 neurons in a hidden layer, and 1 output neuron. The output is a prediction of the daily-averaged electron flux for the tenth day. The neural network was trained using 62 days of data from July 1, 1984, through August 31, 1984, from the SEE spectrometer on the geosynchronous spacecraft 1982-019. The performance of the model was measured by comparing model outputs with measured fluxes over a 6-year period from April 19, 1982, to June 4, 1988. For the entire data set the rms logarithmic error of the neural network is 0.76, and the average logarithmic error is 0.58. The neural network is essentially zero biased, and for accumulation intervals of 3 days or longer the average logarithmic error is less than 0.1. The neural network provides results that are significantly more accurate than those from linear prediction filters. The model has been used to simulate conditions which are rarely observed in nature, such as long periods of quiet (ΣKp = 0) and ideal impulses. It has also been used to make reasonably accurate day-ahead forecasts of the relativistic electron flux at geosynchronous orbit

Lukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models

CERN Document Server

Baianu, I C

2004-01-01

A categorical and Lukasiewicz-Topos framework for Lukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes is proposed. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Lukasiewicz Topos with an n-valued Lukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

Targets Mask U-Net for Wind Turbines Detection in Remote Sensing Images

Science.gov (United States)

Han, M.; Wang, H.; Wang, G.; Liu, Y.

2018-04-01

To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. This convolution neural network (CNN), which is carefully designed to be a wide-field detector, models the pixel class assignment to wind turbines and their context information. The shadow, which is the context information of the target in this study, has been regarded as part of a wind turbine instance. We have trained the target mask U-Net on training dataset, which is composed of down sampled image blocks and instance mask blocks. Some post-processes have been integrated to eliminate wrong spots and produce bounding boxes of wind turbine instances. The evaluation metrics prove the reliability and effectiveness of our method for the average F1-score of our detection method is up to 0.97. The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method.

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

Science.gov (United States)

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

Neural network versus classical time series forecasting models

Science.gov (United States)

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.

Validating neural-network refinements of nuclear mass models

Science.gov (United States)

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.

Modelling of word usage frequency dynamics using artificial neural network

International Nuclear Information System (INIS)

Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

2014-01-01

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 new neural network model for solving random interval linear programming problems.

Science.gov (United States)

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.

DAILY RAINFALL-RUNOFF MODELLING BY NEURAL NETWORKS ...

African Journals Online (AJOL)

K. Benzineb, M. Remaoun

2016-09-01

Sep 1, 2016 ... The hydrologic behaviour modelling of w. Journal of ... i Ouahrane's basin from rainfall-runoff relation which is non-linea networks ... will allow checking efficiency of formal neural networks for flows simulation in semi-arid zone.

Markers of neural degeneration and regeneration in Down ...

African Journals Online (AJOL)

Iman Ehsan Abdel-Meguid

2012-11-02

Nov 2, 2012 ... The Egyptian Journal of Medical Human Genetics www.ejmhg.eg.net ... stem cells (TCSCs), including neural TCSCs and endothelial. TCSCs [10,11]. ..... directs human embryonic stem cell proliferation and differentia- tion into.

REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

Directory of Open Access Journals (Sweden)

S Safinaz

2017-08-01

Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

A Biophysical Neural Model To Describe Spatial Visual Attention

International Nuclear Information System (INIS)

Hugues, Etienne; Jose, Jorge V.

2008-01-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations

Modelling of phase change materials in the Toronto SUI net zero energy house using TRNSYS

Energy Technology Data Exchange (ETDEWEB)

Siddiqui, O.; Fung, A.; Zhang, D. [Ryerson Polytechnic Univ., Toronto, ON (Canada). Dept. of Mechanical and Industrial Engineering

2008-08-15

In the context of building applications, phase change materials (PCM), can be defined as any heat storage material that can absorb a large amount of thermal energy while undergoing a change in phase, such as from a solid to a liquid phase. The incorporation of PCM into the building envelope can enhance occupant comfort through the reduction of indoor temperature fluctuations. It has also been shown to cause a decrease in the overall energy consumption associated with the heating and cooling of buildings. This paper extended the analysis of the impact of using PCM, which has traditionally focused on homes of ordinary construction, to incorporate low to zero energy homes using a model of the Toronto net zero energy house developed in TRNSYS. The paper provided a description of the TRNSYS model/methodology, with reference to the wall layer used in the net zero energy house, and model of the layout of the net zero energy house in TRYNSYS. The TRYNSYS/type 204 PCM component was also presented along with the simulation results in terms of the temperature profile of the third floor of the net zero energy house on a typical winter day with varying PCM concentrations; the temperature profile of the third floor of the net zero energy house on a typical summer day with varying PCM concentrations; yearly heating/cooling load requirements of the net zero energy house for a variety of thermal mass used; temperature profile of the third floor of the net zero energy house on a typical summer day when PCM and concrete slab was used; yearly temperature profile of the third floor of the net zero energy house, illustrating the impact of using PCM; and the yearly heating/cooling load of the net zero energy house as the concentration of PCM was varied. It was concluded that the use of building integrated PCM can reduce temperature fluctuations considerably in the summer but only slightly in the winter. 16 refs., 1 tab., 8 figs.

Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.

Science.gov (United States)

Tiğdemir, Mesut

2014-01-01

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.

End-to-end unsupervised deformable image registration with a convolutional neural network

NARCIS (Netherlands)

de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

2017-01-01

In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial

A neural model for transient identification in dynamic processes with 'don't know' response

International Nuclear Information System (INIS)

Mol, Antonio C. de A.; Martinez, Aquilino S.; Schirru, Roberto

2003-01-01

This work presents an approach for neural network based transient identification which allows either dynamic identification or a 'don't know' response. The approach uses two 'jump' multilayer neural networks (NN) trained with the backpropagation algorithm. The 'jump' network is used because it is useful to dealing with very complex patterns, which is the case of the space of the state variables during some abnormal events. The first one is responsible for the dynamic identification. This NN uses, as input, a short set (in a moving time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide a 'don't know' response. In order to validate the method, a Nuclear Power Plant (NPP) transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor (PWR), was proposed in the validation process it has been considered noisy data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. Another important point studied in this work is that the system has shown to be independent of a trigger signal which indicates the beginning of the transient, thus making it robust in relation to this limitation

Research on user behavior authentication model based on stochastic Petri nets

Science.gov (United States)

Zhang, Chengyuan; Xu, Haishui

2017-08-01

A behavioural authentication model based on stochastic Petri net is proposed to meet the randomness, uncertainty and concurrency characteristics of user behaviour. The use of random models in the location, changes, arc and logo to describe the characteristics of a variety of authentication and game relationships, so as to effectively implement the graphical user behaviour authentication model analysis method, according to the corresponding proof to verify the model is valuable.

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

International Nuclear Information System (INIS)

Jerebko, Anna K.; Summers, Ronald M.; Malley, James D.; Franaszek, Marek; Johnson, C. Daniel

2003-01-01

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Use of artificial neural networks for transport energy demand modeling

International Nuclear Information System (INIS)

Murat, Yetis Sazi; Ceylan, Halim

2006-01-01

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

Directory of Open Access Journals (Sweden)

Stefan Fürtinger

2014-11-01

Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number

Processing of chromatic information in a deep convolutional neural network.

Science.gov (United States)

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

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.

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

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.

Computation and control with neural nets

International Nuclear Information System (INIS)

Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

1989-01-01

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

Deep learning for steganalysis via convolutional neural networks

Science.gov (United States)

Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

2015-03-01

Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

Translating Colored Control Flow Nets into Readable Java via Annotated Java Workflow Nets

DEFF Research Database (Denmark)

Lassen, Kristian Bisgaard; Tjell, Simon

2007-01-01

In this paper, we present a method for developing Java applications from Colored Control Flow Nets (CCFNs), which is a special kind of Colored Petri Nets (CPNs) that we introduce. CCFN makes an explicit distinction between the representation of: The system, the environment of the system, and the ......In this paper, we present a method for developing Java applications from Colored Control Flow Nets (CCFNs), which is a special kind of Colored Petri Nets (CPNs) that we introduce. CCFN makes an explicit distinction between the representation of: The system, the environment of the system......, and the interface between the system and the environment. Our translation maps CCFNs into Anno- tated Java Workflow Nets (AJWNs) as an intermediate step, and these AJWNs are finally mapped to Java. CCFN is intended to enforce the modeler to describe the system in an imperative manner which makes the subsequent...... translation to Java easier to define. The translation to Java preserves data dependencies and control-flow aspects of the source CCFN. This paper contributes to the model-driven software development paradigm, by showing how to model a system, environment, and their interface, as a CCFN and presenting a fully...

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

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.

WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL ...

African Journals Online (AJOL)

This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.

Application of neural networks to software quality modeling of a very large telecommunications system.

Science.gov (United States)

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

Neural Network Models for Free Radical Polymerization of Methyl Methacrylate

International Nuclear Information System (INIS)

Curteanu, S.; Leon, F.; Galea, D.

2003-01-01

In this paper, a neural network modeling of the batch bulk methyl methacrylate polymerization is performed. To obtain conversion, number and weight average molecular weights, three neural networks were built. Each was a multilayer perception with one or two hidden layers. The choice of network topology, i.e. the number of hidden layers and the number of neurons in these layers, was based on achieving a compromise between precision and complexity. Thus, it was intended to have an error as small as possible at the end of back-propagation training phases, while using a network with reduced complexity. The performances of the networks were evaluated by comparing network predictions with training data, validation data (which were not uses for training), and with the results of a mechanistic model. The accurate predictions of neural networks for monomer conversion, number average molecular weight and weight average molecular weight proves that this modeling methodology gives a good representation and generalization of the batch bulk methyl methacrylate polymerization. (author)

Towards semen quality assessment using neural networks

DEFF Research Database (Denmark)

Linneberg, Christian; Salamon, P.; Svarer, C.

1994-01-01

The paper presents the methodology and results from a neural net based classification of human sperm head morphology. The methodology uses a preprocessing scheme in which invariant Fourier descriptors are lumped into “energy” bands. The resulting networks are pruned using optimal brain damage. Pe...

Quantitative phase microscopy using deep neural networks

Science.gov (United States)

Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

2018-02-01

Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

Science.gov (United States)

Zhelavskaya, I. S.; Shprits, Y.; Spasojevic, M.

2017-12-01

We present 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 October 1, 2012 - July 1, 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 datasets 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). We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

A biologically inspired neural net for trajectory formation and obstacle avoidance.

Science.gov (United States)

Glasius, R; Komoda, A; Gielen, S C

1996-06-01

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

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...

Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers

International Nuclear Information System (INIS)

Mikulandrić, Robert; Lončar, Dražen; Böhning, Dorith; Böhme, Rene; Beckmann, Michael

2014-01-01

Highlights: • 2 Different equilibrium models are developed and their performance is analysed. • Neural network prediction models for 2 different fixed bed gasifier types are developed. • The influence of different input parameters on neural network model performance is analysed. • Methodology for neural network model development for different gasifier types is described. • Neural network models are verified for various operating conditions based on measured data. - Abstract: The number of the small and middle-scale biomass gasification combined heat and power plants as well as syngas production plants has been significantly increased in the last decade mostly due to extensive incentives. However, existing issues regarding syngas quality, process efficiency, emissions and environmental standards are preventing biomass gasification technology to become more economically viable. To encounter these issues, special attention is given to the development of mathematical models which can be used for a process analysis or plant control purposes. The presented paper analyses possibilities of neural networks to predict process parameters with high speed and accuracy. After a related literature review and measurement data analysis, different modelling approaches for the process parameter prediction that can be used for an on-line process control were developed and their performance were analysed. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed. Measurement data for the model development, verification and performance analysis were derived from biomass gasification plant operated by Technical University Dresden

Artificial neural network cardiopulmonary modeling and diagnosis

Science.gov (United States)

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.

A dynamic neural field model of temporal order judgments.

Science.gov (United States)

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).

Coupling of Petri Net Models of the Mycobacterial Infection Process and Innate Immune Response

Directory of Open Access Journals (Sweden)

Rafael V. Carvalho

2015-04-01

Full Text Available Computational and mathematical modeling is important in support of a better understanding of complex behavior in biology. For the investigation of biological systems, researchers have used computers to construct, verify, and validate models that describe the mechanisms behind biological processes in multi-scale representations. In this paper we combine Petri net models that represent the mycobacterial infection process and innate immune response at various levels of organization, from molecular interaction to granuloma dissemination. In addition to the conventional graphical representation of the Petri net, the outcome of the model is projected onto a 3D model representing the zebrafish embryo. In this manner we provide a visualization of the process in a simulation framework that portrays the infection in the living system.

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.

STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK

Directory of Open Access Journals (Sweden)

J. Zhang

2018-04-01

Full Text Available Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Study on the Classification of GAOFEN-3 Polarimetric SAR Images Using Deep Neural Network

Science.gov (United States)

Zhang, J.; Zhang, J.; Zhao, Z.

2018-04-01

Polarimetric Synthetic Aperture Radar (POLSAR) imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Real-time process optimization based on grey-box neural models

Directory of Open Access Journals (Sweden)

F. A. Cubillos

2007-09-01

Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

Gait pattern recognition in cerebral palsy patients using neural network modelling

International Nuclear Information System (INIS)

Muhammad, J.; Gibbs, S.; Abboud, R.; Anand, S.

2015-01-01

Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network models which can recognise the gait patterns from pre- and post-treatment and the normal ones. Neural network is a method which works on the principle of learning from experience and then uses the obtained knowledge to predict the unknown. Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre- and post-treatment. A group of twenty-six normal subjects also participated in this study as control group. All subjects gait was analysed using Vicon Nexus to obtain the gait parameters and kinetic and kinematic parameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different models were built using different parameters. The gait was categorised as three patterns, i.e., normal, pre- and post-treatments. Result: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g., the models using joint power and moments, had lower rate in recognition of gait patterns with approximately 70-90% successful ratio. Conclusion: Neural network model can be used in clinical practice to recognise the gait pattern for cerebral palsy patients. (author)

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

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.

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

Science.gov (United States)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

Robust recurrent neural network modeling for software fault detection and correction prediction

International Nuclear Information System (INIS)

Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

2007-01-01

Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set

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.

Generating Seismograms with Deep Neural Networks

Science.gov (United States)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

Computational Models and Emergent Properties of Respiratory Neural Networks

Science.gov (United States)

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Forecast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

International Nuclear Information System (INIS)

Vannucci, A.; Oliveira, K.A.; Tajima, T.; Tajima, Y.J.

2001-01-01

A feed-forward neural network is used to forecast major and minor disruptions in TEXT tokamak discharges. Using the experimental data of soft X-ray signals as input data, the neural net is trained with one disruptive plasma discharge, while a different disruptive discharge is used for validation. After proper training, the net works with the same set of weights, it is then used to forecast disruptions in two other different plasma discharges. It is observed that the neural net is capable of predicting the onset of a disruption up to 3.12 ms in advance. From what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism is associated with an increase in the m=2 magnetic island, that disturbs the central part of the plasma column afterwards, or the initial perturbation has first occurred in the central part of the plasma column and then the m=2 MHD mode is destabilized. (author)

Hindcasting of storm waves using neural networks

Digital Repository Service at National Institute of Oceanography (India)

Rao, S.; Mandal, S.

Department NN neural network net i weighted sum of the inputs of neuron i o k network output at kth output node P total number of training pattern s i output of neuron i t k target output at kth output node 1. Introduction Severe storms occur in Bay of Bengal...), forecasting of runoff (Crespo and Mora, 1993), concrete strength (Kasperkiewicz et al., 1995). The uses of neural network in the coastal the wave conditions will change from year to year, thus a proper statistical and climatological treatment requires several...

Comparing Neural Networks and ARMA Models in Artificial Stock Market

Czech Academy of Sciences Publication Activity Database

Krtek, Jiří; Vošvrda, Miloslav

2011-01-01

Roč. 18, č. 28 (2011), s. 53-65 ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

Optimization of artificial neural networks for the reconstruction of the neutrons spectrum and their equivalent doses

International Nuclear Information System (INIS)

Reyes A, A.; Ortiz R, J. M.; Reyes H, A.; Castaneda M, R.; Solis S, L. O.; Vega C, H. R.

2014-08-01

In this work was used the robust design methodology of artificial neural networks to determine a good topology of net able to solve with efficiency the problems of neutrons spectrometry and dosimetry. For the design of the topology of optimized net 36 different net architectures based on an orthogonal arrangement with a configuration L 9 (3 4 ), L 4 (3 2 ) were trained. For the training of the neural networks, was used a computer code developed in the ambient of Mat lab programming, which automates the process and analysis of the information, reducing the time used in this activity considerably for the investigator. For the training of the propagation nets forward was utilized a neutrons spectrum compendium published by the International Atomic Energy Agency, where of the total 80% was used for the training and 20% for the test, it trained with an inverse propagation algorithm being the entrance data the count rates corresponding to the 7 spheres of the spectrometric system of Bonner spheres, as exit data, the neural network obtains the neutrons spectrum expressed in 60 energy groups and are calculated of simultaneous way 15 dosimetric quantities. (Author)

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Directory of Open Access Journals (Sweden)

Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Manoonpong, Poramate

2016-01-01

expensive algorithms. We present a novel bioinspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural...

Bias-dependent hybrid PKI empirical-neural model of microwave FETs

Science.gov (United States)

Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

2011-10-01

Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.

Models of neural dynamics in brain information processing - the developments of 'the decade'

Energy Technology Data Exchange (ETDEWEB)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B [Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation); Ivanitskii, Genrikh R [Institute for Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation)

2002-10-31

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

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...

Discrete, continuous, and hybrid petri nets

CERN Document Server

David, René

2004-01-01

Petri nets do not designate a single modeling formalism. In fact, newcomers to the field confess sometimes to be a little puzzled by the diversity of formalisms that are recognized under this "umbrella". Disregarding some extensions to the theoretical modeling capabilities, and looking at the level of abstraction of the formalisms, Condition/Event, Elementary, Place/Transition, Predicate/Transition, Colored, Object Oriented... net systems are frequently encountered in the literature. On the other side, provided with appropriate interpretative extensions, Controled Net Systems, Marking Diagrams (the Petri net generalization of State Diagrams), or the many-many variants in which time can be explicitly incorporated -Time(d), Deterministic, (Generalized) Stochastic, Fuzzy...- are defined. This represents another way to define practical formalisms that can be obtained by the "cro- product" of the two mentioned dimensions. Thus Petri nets constitute a modeling paradigm, understandable in a broad sense as "the total...

Teaching methodology for modeling reference evapotranspiration with artificial neural networks

OpenAIRE

Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

2015-01-01

[EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

Modelling of solar energy potential in Nigeria using an artificial neural network model

International Nuclear Information System (INIS)

Fadare, D.A.

2009-01-01

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.