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نتیجه جستجو - شبکه عصبی

ردیف | عنوان | نوع |
---|---|---|

1 |
Establishment and application of intelligent city building information model based on BP neural network model
ایجاد و کاربرد مدل اطلاعات هوشمند شهرسازی براساس مدل شبکه عصبی BP-2020 The construction of smart cities in our country has received extensive attention. Under the situation that
smart cities are vigorously promoted nowadays, compared with traditional construction and operation and
maintenance methods, building information model (BIM) technology is more suitable to serve as an important
foundation for intelligent management in the whole process of construction projects. BIM is an abbreviation
for building information model. BIM relies on a variety of digital technologies, which can be used to realize
information modeling of urban buildings and infrastructure. The efficiency of information exchange in the
process of intelligence construction ensures the integrity and accuracy of information data exchange and
maintains the consistency of information data exchange. Data and information have objectivity, applicability,
transferability, and sharing. Geographic data is a digital representation of various geographical features and
phenomena and their relationships. BIM is a digital representation of physical and functional characteristics
of a facility. It can It is used as a shared knowledge resource for facility information. It becomes a reliable
basis for facility life-cycle decision-making. Input BP neural network, and then learn and train by BP neural
network. Keywords: BP neural network | Smart city | Building information model |
مقاله انگلیسی |

2 |
A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020 Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction
in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women
and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly,
the disabled, and pediatric sleep apnea patients.
Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical
system (MEMS) based acceleration sensor. It records the value of acceleration by measuring
the movements of the diaphragm in three axes during the respiratory. The measurements are
carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement
results. An artificial neural network model was designed to determine the apnea event. For the number
of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden
neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that
three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer.
Results: A study group was formed of 5 patients (having different characteristics (age, height, and body
weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration
data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and
detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train
an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then
Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data.
Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration
values. Measurements are performed by the MEMS-based accelerometer and Industrial
Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients.
The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with
1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check
the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark
shows us that trained ANN successfully detects apnea events. One of the contributions of this study
to literature is that only ACC data are used in the ANN training step. After training for one patient, the
ANN system can monitor the apnea event situation on-line for others. Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making |
مقاله انگلیسی |

3 |
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020 Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based
models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN
models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered
neural network model is developed to predict the permeate flux in forward osmosis. The developed model is
tested for its generalization capability by including lab-scale experimental data from several published studies.
Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed
solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and
the draw solution and temperature of the feed solution and the draw solution. The development of optimum
network architecture is supported by studying the impact of the number of neurons and hidden layers on the
neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency
of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of
the model is tested against untrained published data. The performance of the ANN model is compared with a
transport-based model in the literature. A simple machine learning technique such as a multiple linear regression
(MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its
ability to form a complex relationship between inputs and output better than MLR. Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning |
مقاله انگلیسی |

4 |
Random recurrent neural networks with delays
شبکه های عصبی مکرر تصادفی با تأخیر-2020 An infinite lattice model of a recurrent neural network with random connection strengths between neurons is developed and analyzed. To incorporate the presence of various type of delays in the neural networks, both discrete and distributed time varying delays are considered in the model. For the existence of random pullback attractors and periodic attractors, the nonlinear terms of the resulting system are not expected to be Lipschitz continuous, but only satisfy a weaker continuity assumption along with growth conditions, under which the uniqueness of the underlying Cauchy problem may not hold. Then after extending the concept and theory of monotone multi-valued semiflows to the random context, the structure of random pullback attractors with or without periodicity is investigated. In particular, the existence and stability properties of extremal random complete trajectories are studied. Keywords:Random recurrent neural network | Random attractor | Multi-valued non compact random dynamical system | Extremal random complete trajectory | Variable delay | Monotone |
مقاله انگلیسی |

5 |
Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020 The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of
the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal
smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the
smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same
value, which not only reflects the relationship between the training samples of the same pattern, but also
highlights the difference between the training samples of different modes. Two probabilistic neural network
models are applied to the ship impact environment prediction respectively. The results show that the classification
effect of multiple smoothing factors is further improved than the single smoothing factor network. Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm |
مقاله انگلیسی |

6 |
Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet
توسعه یک روش اسپکتروفتومتری با کمک شیمیایی برای تعیین کمی همزمان آملودیپین و والرسارتان در قرص تجاری-2020 In this study, two drugs named Amlodipine (AML) and Valsartan (VAL) related to the high blood
pressure were simultaneously determined in synthetic mixtures and Valzomix tablet. For this
purpose, the chemometric-assisted spectrophotometric method was developed without any prepreparation.
Artificial intelligence techniques, including artificial neural network (ANN) and
least squares support vector machine (LS-SVM) as chemometrics procedures were proposed.
Feed-forward back-propagation neural network (FFBP-NN) with two different algorithms, containing
Levenberg–Marquardt (LM) and gradient descent with momentum and adaptive learning
rate backpropagation (GDX) was applied. To select the best model, several layers and neurons
were investigated. The results revealed that layer = 5 with 6 neurons and layer = 2 with 10
neurons had lower mean square error (MSE) (1.41 × 10−24, 1.16 × 10-23) for AML and VAL,
respectively. In the LS-SVM method, gamma (γ) and sigma (σ) parameters were optimized. γ and
σ were obtained 50, 30 and 40, 40 with the root mean square error (RMSE) of 0.4290 and 0.5598
for AML and VAL, respectively. Analysis of the pharmaceutical formulation was evaluated
through the chemometrics methods and high-performance liquid chromatography (HPLC) as a
reference technique. The obtained results were statistically compared with each other using the
one-way analysis of variance (ANOVA) test. There were no significant differences between them
and the proposed method was satisfactory for estimating the components of the Valzomix tablet. Keywords: Spectrophotometry | Amlodipine | Valsartan | Artificial neural network | Least squares support vector machine |
مقاله انگلیسی |

7 |
Neural network with multiple connection weights
شبکه های عصبی با وزن اتصال چندگانه-2020 Biological studies have shown that the interaction between neurons are based on neurotransmitters, which transmit signals between neurons, and that one neuron sends information to another neuron by releasing a number of different neurotransmitters, which play different roles. Motivated by this biolog- ical discovery, a novel neural networks model is proposed by extending the dimension of connection weights from one to multiple, i.e. there are multiple not only one connections between each two units. The number of dimensions of connection weight represents the number of categories of neurotransmit- ters and different com ponents of the weight correspond to different neurotransmitters. In order to make these neurotransmitters collaborate and compete appropriately, the input and output for each unit in our proposed model have been heuristically defined. From the biological perspective, the proposed neural network is much closer to biological neural network. From the viewpoint of new model structure, the characteristic that the activation of each hidden unit is based on several filters, can improve the inter- pretability of features learned by the proposed neural network. Experimental results on MNIST, NORB and several other data sets have demonstrated that the performances of traditional neural networks can be improved by extending the dimension of connection weight between units, and the idea of multiple connection weights provides a new paradigm for the design of neural networks. Keywords: Neural network | Neurotransmitter | Interpretability | Extending dimension |
مقاله انگلیسی |

8 |
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
از ساختار شیمیایی گرفته تا پیش بینی کمی از خواص پلیمر از طریق شبکه های عصبی در هم تنیده -2020 In this work convolutional-fully connected neural networks were designed and trained to predict the glass
transition temperature of polymers based only on their chemical structure. This approach has shown to successfully
predict the Tg of unknown polymers with average relative errors as low as 6%. Several networks with
different architecture or hiperparameters were successfully trained using a previously studied glass transition
temperatures dataset for validation, and then the same method was employed for an extended dataset, with
larger Tg dispersion and polymer’s structure variability. This approach has shown to be accurate and reliable, and
does not require any time consuming or expensive measurements and calculations as inputs. Furthermore, it is
expected that this method can be easily extended to predict other properties. The possibility of predicting the
properties of polymers not even synthesized will save time and resources for industrial development as well as
accelerate the scientific understanding of structure-properties relationships in polymer science. Keywords: QSPR | Properties prediction | Deep learning | Neural network | Smart design |
مقاله انگلیسی |

9 |
Wake modeling of wind turbines using machine learning
مدل سازی توربین های بادی با استفاده از یادگیری ماشین-2020 In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics)
simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is
proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation
(BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions
and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model
ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes
equations) simulations coupled with a modified k − ε turbulence model to provide big datasets of wake flow for
training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is
validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines.
In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as
input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are
taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the
spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev
wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased
wake model show good agreement with the numerical simulations and measurement data, indicating that
the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake
flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions. Keywords: Wind turbine wake | Wake model | Artificial neural network (ANN) | Machine learning | ADM-R (actuator-disk model with rotation) | model | Computational fluid dynamics (CFD) |
مقاله انگلیسی |

10 |
Implementation of homeostasis functionality in neuron circuit using doublegate device for spiking neural network
اجرای عملکرد هموستاز در مدار نورون با استفاده از دستگاه دروازه دو تایی برای شبکه عصبی spiking -2020 The homeostatic neuron circuit using a double-gate MOSFET is proposed to imitate a homeostasis functionality
of a biological neuron in spiking neural networks (SNN) based on a spike-timing dependent plasticity (STDP).
The threshold voltage (Vth) of the double-gate MOSFET is controlled by independent two-gate biases (VG1 and
VG2). By using Vth change of the double-gate MOSFET in the neuron circuits, the fire rate of the output neuron is
controlled. The homeostasis functionality is implemented by the operation of multi-neuron system based on the
proposed neuron circuit. Through the SNN based on STDP using MNIST datasets, it is demonstrated that the
recognition rate (~91%) of the SNN with the proposed homeostasis functionality is higher than that (~79%) of
the SNN without the proposed homeostasis functionality. Also, the results of the recognition rate with the
variations (σ/μ < 0.5) of the synaptic devices and the initial Vth of neuron circuits show a low degradation
(1 ~ 3%) in the recognition rate. Thus, it is demonstrated that the homeostasis functionality of the proposed
neuron circuit has the immunity to variations (σ/μ < 0.5) of the synaptic devices and the neuron circuits in the
SNN based on STDP. Keywords: Double-gate MOSFET | Neuron circuit | Homeostasis functionality | Pattern recognition | Spiking neural networks (SNNs) |
مقاله انگلیسی |