Journal articles: 'Feed forward layer'

1

Safi, Youssef, and Abdelaziz Bouroumi. "Evolutionary single hidden-layer feed forward networks." International Journal of Innovative Computing and Applications 6, no. 2 (2014): 73. http://dx.doi.org/10.1504/ijica.2014.066497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Hasbi, Yasin, Warsito Budi, and Santoso Rukun. "Feed Forward Neural Network Modeling for Rainfall Prediction." E3S Web of Conferences 73 (2018): 05017. http://dx.doi.org/10.1051/e3sconf/20187305017.

Full text

Abstract:

Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.

APA, Harvard, Vancouver, ISO, and other styles

3

Svozil, Daniel, Vladimír Kvasnicka, and Jir̂í Pospichal. "Introduction to multi-layer feed-forward neural networks." Chemometrics and Intelligent Laboratory Systems 39, no. 1 (1997): 43–62. http://dx.doi.org/10.1016/s0169-7439(97)00061-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Behunkou, U. I. "Loan classification using a feed-forward neural network." Informatics 21, no. 1 (2024): 83–104. http://dx.doi.org/10.37661/1816-0301-2024-21-1-83-104.

Full text

Abstract:

Objectives. The purpose of the study is to construct and study the use of a feed-forward neural network to solve the problem of loan classification, as well as to conduct a comparative analysis of the neural networkbased approach with the existing approach based on logistic regression.Methods. Based on a feed-forward neural network using historical data on loans issued, the following metrics are calculated: cost function, Accuracy, Precision, Recall, and measure, calculated on Precision and Recall values. Polynomial parameters and the principal component method are used to determine the optimal set of input data for the studied neural network.Results. The impact of data normalization on the final result was analyzed, the influence of the number of units in the hidden layer on the outcome was evaluated using a two-stage method and the Monte Carlo method, the effect of balanced data use was determined, the optimal threshold value for output layer of the neural network under investigation was calculated, the optimal activation function for the hidden layer units was found, the effect of increasing input indicators through missing values imputation and the use of polynomials of varying degrees was studied and the redundancy in the existing set of input indicators was analyzed.Conclusion. Based on the results of the research, we can conclude that the use of a direct distribution network to solve problems of loan classification is appropriate. Compared to logistic regression, implementing a solution using a feed-forward neural network requires more time and computing resources. However, the obtained most important values of Accuracy and measure are higher than those calculated using logistic regression [1].

APA, Harvard, Vancouver, ISO, and other styles

5

Hayat, Safdar. "Phone numbers classificationwith feed-forward neural networks." Foundation University Journal of Engineering and Applied Sciences (HEC Recognized Y Category , ISSN 2706-7351) 1, no. 2 (2021): 1–5. http://dx.doi.org/10.33897/fujeas.v1i2.340.

Full text

Abstract:

A neural network (NN)-based method for phone number classification or recognition is provided in this paper. The used network is a one-hidden-layer multilayer perceptron (MLP) classifier. Its training is based on backpropagation learning. I present the results of a Feed Forward Neural Network trained to classify phone numbers into four categories: Different training data were pre-processed and then tested to distinguish between four classes/patterns of phone numbers in order to train the FFNN. My goal is to provide a coalescence of the published research in this field and to arouse further research interest in and efforts to research the identified topics.

APA, Harvard, Vancouver, ISO, and other styles

6

Karakida, Ryo, Yasuhiko Igarashi, Kenji Nagata, and Masato Okada. "Inter-Layer Correlation in a Feed-Forward Network with Intra-Layer Common Noise." Journal of the Physical Society of Japan 82, no. 6 (2013): 064007. http://dx.doi.org/10.7566/jpsj.82.064007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Indurkhya, Nitin, and Sholom M. Weiss. "Heuristic configuration of single hidden-layer feed-forward neural networks." Applied Intelligence 2, no. 4 (1992): 325–31. http://dx.doi.org/10.1007/bf00058649.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Pham, Minh Tuan, Syouhei Nishihama, and Kazuharu Yoshizuka. "Removal of Chromium from Water Environment by Forward Osmosis System." MATEC Web of Conferences 333 (2021): 04007. http://dx.doi.org/10.1051/matecconf/202133304007.

Full text

Abstract:

Forward osmosis (FO) technology has been applied for removal of chromium (Cr) from water environment. Comparison of the removal efficiency of Cr(VI) and Cr(III) was investigated by changing several operational conditions. The pH of feed solution plays an important role in rejection of Cr. The Cr(VI) rejection was increased with increasing pH, while Cr(III) rejection was stable. It also demonstrated that the rejection of Cr was higher when the membrane active layer faces the feed solution compared to the rejection when the membrane active layer faces the draw solution.

APA, Harvard, Vancouver, ISO, and other styles

9

Pham, Minh Tuan, Syouhei Nishihama, and Kazuharu Yoshizuka. "Removal of Chromium from Water Environment by Forward Osmosis System." MATEC Web of Conferences 333 (2021): 04007. http://dx.doi.org/10.1051/matecconf/202133304007.

Full text

Abstract:

Forward osmosis (FO) technology has been applied for removal of chromium (Cr) from water environment. Comparison of the removal efficiency of Cr(VI) and Cr(III) was investigated by changing several operational conditions. The pH of feed solution plays an important role in rejection of Cr. The Cr(VI) rejection was increased with increasing pH, while Cr(III) rejection was stable. It also demonstrated that the rejection of Cr was higher when the membrane active layer faces the feed solution compared to the rejection when the membrane active layer faces the draw solution.

APA, Harvard, Vancouver, ISO, and other styles

10

Mahmood, M. F., and Z. Ahmad. "Application of Multi-Layer Feed Forward Neural Network (MLFNN) for the." Nucleus 54, no. 1 (2017): 10–15. https://doi.org/10.71330/thenucleus.2017.92.

Full text

Abstract:

The geophysical formation evaluation plays a fundamental role in hydrocarbon exploration. Porosity is one of the main parameters that determine the amount of oil present in a rock formation. Accurate determination of porosity is a difficult problem due to failure in understanding of spatial porosity parameter distribution. Multi-layer feed forward neural network (MLFN) proved to be a powerful tool for mapping porosity across the whole field and proved to be a powerful tool for mapping complicated relationships in reservoir. In MLFN three layers are involved that is an input layer, an output layer and a variable number of hidden layers. Input for training eight external attributes are used which are P-impedance, S-impedance, density, fluid, lithology impedance, lamda-rho, mu-rho, and Vp/Vs. Five nodes are used in hidden layer and one output node for mapping total porosity of Badin gas field. In this study 3D cube of Badin field and 3 wells are used. The findings proved competence of multi-layer feed forward neural network in the porosity prediction process with an average error of 0.014 [v/v] and the correlation coefficient of 0.91 and helped in studying the lateral variations in the porosity along the reservoir. The A sands show same porosity values along both the well locations, while for B sand the porosity value decreases from Zaur-01 to Chakri-01 well while for C sand the porosity value increases from Zaur-01 to Chakri-01 well.

APA, Harvard, Vancouver, ISO, and other styles

11

Tian, Miao, Tao Ma, Kunli Goh, Zhiqiang Pei, Jeng Yi Chong, and Yi-Ning Wang. "Forward Osmosis Membranes: The Significant Roles of Selective Layer." Membranes 12, no. 10 (2022): 955. http://dx.doi.org/10.3390/membranes12100955.

Full text

Abstract:

Forward osmosis (FO) is a promising separation technology to overcome the challenges of pressure-driven membrane processes. The FO process has demonstrated profound advantages in treating feeds with high salinity and viscosity in applications such as brine treatment and food processing. This review discusses the advancement of FO membranes and the key membrane properties that are important in real applications. The membrane substrates have been the focus of the majority of FO membrane studies to reduce internal concentration polarization. However, the separation layer is critical in selecting the suitable FO membranes as the feed solute rejection and draw solute back diffusion are important considerations in designing large-scale FO processes. In this review, emphasis is placed on developing FO membrane selective layers with a high selectivity. The effects of porous FO substrates in synthesizing high-performance polyamide selective layer and strategies to overcome the substrate constraints are discussed. The role of interlayer in selective layer synthesis and the benefits of nanomaterial incorporation will also be reviewed.

APA, Harvard, Vancouver, ISO, and other styles

12

Priramadhi, Rizki Ardianto, and Denny Darlis. "Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification." IJAIT (International Journal of Applied Information Technology) 5, no. 01 (2022): 34. http://dx.doi.org/10.25124/ijait.v5i01.3220.

Full text

Abstract:

In this research, a Feed-Forward Artificial Neural Network design was implemented on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board and prototyped blood type classification device. This research uses blood sample images as a system input. The system was built using VHSIC Hardware Description Language to describe the feed-forward propagation with a backpropagation neural network algorithm. We use three layers for the feed-forward ANN design with two hidden layers. The hidden layer designed has two neurons. In this study, the accuracy of detection obtained for four-type blood image resolutions results from 86%-92%, respectively.

APA, Harvard, Vancouver, ISO, and other styles

13

Jabar, Asia L., and Tarik A. Rashid. "A Modified Particle Swarm Optimization with Neural Network via Euclidean Distance." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 1 (2018): 4. http://dx.doi.org/10.3991/ijes.v6i1.8080.

Full text

Abstract:

In this paper, a new modified model of Feed Forward Neural Network with Particle Swarm Optimization via using Euclidean Distance method (FNNPSOED) is used to better handle a classification problem of the employee’s behavior. The Particle Swarm Optimization (PSO) as a natural inspired algorithm is used to support the Feed Forward Neural Network (FNN) with one hidden layer in obtaining the optimum weights and biases using different hidden layer neurons numbers. The key reason of using ED with PSO is to take the distance between each two-feature value then use this distance as a random number in the velocity equation for the velocity value in the PSO algorithm. The FNNPSOED is used to classify employees’ behavior using 29 unique features. The FNNPSOED is evaluated against the Feed Forward Neural Network with Particle Swarm Optimization (FNNPSO). The FNNPSOED produced satisfactory results.

APA, Harvard, Vancouver, ISO, and other styles

14

Rani, Dr K. SheelaSobana, and Dr S. Anila. "Comparison of feed forward, Cascade forward and Layer Recurrent Algorithm model for breast cancer prediction." IOP Conference Series: Materials Science and Engineering 705 (December 2, 2019): 012055. http://dx.doi.org/10.1088/1757-899x/705/1/012055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Batubara, Qunazatul Shima, and Tomi Erfando. "The Successful Prediction Of Waterflooding Using A Feed Forward Algorithm." Aceh International Journal of Science and Technology 12, no. 2 (2023): 188–96. http://dx.doi.org/10.13170/aijst.12.2.30813.

Full text

Abstract:

Waterflooding is one of the most frequently used EOR methods to increase oil recovery because it can increase 30% -60% of total production. It is necessary to apply a production system performance prediction approach to minimize uncertainty in increasing production figures, such as analytical methods and numerical methods. Artificial Intelligence in the world of oil and gas is not a new thing, but it has often been used in various fields such as exploration, drilling, production, and reservoirs. So this is the basis for the prediction of the success of waterflooding research carried out. The purpose of this research was to predict the success rate of waterflooding using an Artificial Neural Network (ANN). The method used in this study is the simulation research method using CMG Imex for reservoir simulation modeling, running CMG CMOST for 500 sensitivity data with the input of seven parameters of compressibility, horizontal permeability, vertical permeability, pressure injection, injection rate, thickness, oil saturation, and the output is recovery factor using Artificial Neural Network (ANN) with a ratio of 70% of the RF calculation model results for training and 30% model results for testing. In order to get optimal prediction results, trial, and error were carried out on the number of hidden layer nodes, so that optimal and stable hidden layer nodes were obtained at node 10 with RMSE values of 0.339035 for training and 0.442663 for testing and MAPE for training 1.15% and 1.62% for testing. The statistical analysis value is 0.906139 for training and 0.899525 for testing data. It can be concluded from this study that the use of ANN in predictions using ten hidden layer nodes proved to be very good and successful, and predictions in this study were classified as High Accurate Prediction.

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Zongyan, and Matt Best. "Structure optimisation of input layer for feed-forward NARX neural network." International Journal of Modelling, Identification and Control 25, no. 3 (2016): 217. http://dx.doi.org/10.1504/ijmic.2016.075814.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Asadi, Roya, Sameem Abdul Kareem, Mitra Asadi, and Shokoofeh Asadi. "A Single-Layer Semi-Supervised Feed Forward Neural Network Clustering Method." Malaysian Journal of Computer Science 28, no. 3 (2015): 189–212. http://dx.doi.org/10.22452/mjcs.vol28no3.2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Moraru, Luminita, Simona Moldovanu, and Andreea-Monica (Lăzărescu) Dincă. "Feed-Forward Back Propagation Network for the prediction of diabetic retinopathy disorder." Annals of the ”Dunarea de Jos” University of Galati Fascicle II Mathematics Physics Theoretical Mechanics 44, no. 1 (2021): 67–74. http://dx.doi.org/10.35219/ann-ugal-math-phys-mec.2021.1.10.

Full text

Abstract:

Some retina disorders mainly involve some blocked blood clots so that, the retinal vessels change their structure, being unable to completely nourish the retina. For an accurate investigation of retina disorders, the extraction of the retinal vessel anatomical structures or lesions is the main task. This paper reports a combination of various features extracted from retinal images, that are further used to train a Feed-Forward Back Propagation Network (FFBPN) as a decision system. The main goal is determining the combination of the appropriate features for more accurate classification of healthy and diseased patients. To achieve this goal, 120 binary images covering both categories of patients that belong to the STARE (Structured Analysis of the Retina) database were analyzed. The input data are the number of ridges, bifurcation, and bridges for retinal vessel pattern recognition. The FFBPNs with 4, 8, 12, 16, and 20 neurons in the hidden layer are trained. The FFBNP architecture with 12 neurons in the hidden layer, using the tansig transfer function in the hidden layer and linear transfer function in the output layer provides the most appropriate model for retinopathy disease classification. The correlation between the number of ridges and bridges computed for healthy patients (as actual values) and the number of ridges and bridges for diabetic patients (as predicted values) provides the best result, a regression coefficient (R) of 0. 8575 and a mean-square error (MSE) of 0.00163.

APA, Harvard, Vancouver, ISO, and other styles

19

Chang, Z. H., Y. H. Teow, S. P. Yeap, and J. Y. Sum. "Membrane Fouling – The Enemy of Forward Osmosis." Journal of Applied Membrane Science & Technology 25, no. 2 (2021): 73–88. http://dx.doi.org/10.11113/amst.v25n2.220.

Full text

Abstract:

Forward osmosis (FO) is an osmotically driven membrane separation process. It is potentially applied in various industries for nutrient recovery and water reclamation. Although FO showed a lesser fouling tendency than other pressure-driven membrane processes, the solutes in the feed solution would still deposit on the membrane surface, forming a fouling layer that resists water permeation. For that reason, fouling mitigation is a trending issue in the FO process. A better understanding of the fouling mechanism is required before opting for the appropriate strategy to mitigate it. This article describes the fouling mechanism based on different foulant presented in the feed, followed by a method in relieving fouling in the FO process.

APA, Harvard, Vancouver, ISO, and other styles

20

Et. al., Jackie D. Urrutia. "An Analytical Study On Forecasting Exchange Rate In The Philippines Using Multi-Layer Feed Forward Neural Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5357–77. http://dx.doi.org/10.17762/turcomat.v12i3.2182.

Full text

Abstract:

Exchange Rate is one of the economic indicators in the Philippines. It is the value of the nation’s currency versus the currency of another country or economic zone. This study aims to forecast the monthly Exchange Rate (y) of the Philippines from November 2018 to December 2023 using Multiple Linear Regression and Multi-Layer Feed Forward Neural Network. The researchers investigate the behaviour of each independent variables – Inflation Rate (x1), Balance of Payments (x2), Interest Rate (x3), Producer’s Price Index (x4), Export (x5), Import (x6), Money Supply (x7), and Consumer’s Price Index (x8) from Philippine Statistics Authority (PSA) starts from January 2007 up to October 2018. Multiple Linear Regression (MLR) was used to identify significant predictors among these independent variables. The Exchange Rate (y) had undergone first difference transformation. Upon running the regression analysis, it has concluded that only two independent variables are significant predictors, namely: Balance of Payments (x2) and Import (x6). Through these significant predictors, the MLR model was formulated. On the other hand, Multi-Layer Feed forward Neural Network (MFFNN) was also used to determine the forecasted values of Exchange Rate (y) for the next five years (2018-2023) given the said independent variables and obtained a model. The researchers compared the model of Multiple Linear Regression and Multi-Layer Feed Forward Neural Network by evaluating the forecasting accuracy of each method.It was concluded that Multi-Layer Feed forward Neural Network is the best fitting model for forecasting the Exchange rate (y) in the Philippines. This paper will serve as a tool of awareness for the government to forsee the trend of Exchange Rate in the Philippines on the next five years (2018-2023) for Monetary Policy making and to prevent the possible depreciation of peso vs. dollar.

APA, Harvard, Vancouver, ISO, and other styles

21

Liqaa, Saadi Mezher. "Design and implementation hamming neural network with VHDL." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2022): 1469–79. https://doi.org/10.11591/ijeecs.v19.i3.pp1469-1479.

Full text

Abstract:

Hamming Neural Network is type of artificial neural network consist of two types of layers (Feed Forward Layers and Recurrent Layer). In this paper, two inputs of patterns in binary number were used. In the first layer, two neurons and pure line function were used. In the second layer, three neurons and positive line function were used. Also applied Hamming Neural networks algorithm in three simulation methods (Logical gate method, software program coding method and instant block diagram method). In this work in VHDL software program was used and FPGA hardware used.

APA, Harvard, Vancouver, ISO, and other styles

22

Dietz Saldanha, Emmanuelle-Anna, Steffen Hölldobler, Carroline Dewi Puspa Kencana Ramli, and Luis Palacios Medinacelli. "A Core Method for the Weak Completion Semantics with Skeptical Abduction." Journal of Artificial Intelligence Research 63 (September 28, 2018): 51–86. http://dx.doi.org/10.1613/jair.1.11236.

Full text

Abstract:

   The Weak Completion Semantics is a novel cognitive theory which has been successfully applied to the suppression task, the selection task, syllogistic reasoning, the belief bias effect, spatial reasoning as well as reasoning with conditionals. It is based on logic programming with skeptical abduction. Each program admits a least model under the three-valued Lukasiewicz logic, which can be computed as the least fixed point of an appropriate semantic operator. The semantic operator can be represented by a three-layer feed-forward network using the core method. Its least fixed point is the unique stable state of a recursive network which is obtained from the three-layer feed-forward core by mapping the activation of the output layer back to the input layer. The recursive network is embedded into a novel network to compute skeptical abduction. This paper presents a fully connectionist realization of the Weak Completion Semantics.  

APA, Harvard, Vancouver, ISO, and other styles

23

Wen, Zhiqing, Pochi Yeh, and Xiangyang Yang. "Optoelectronic three-layer feed-forward perceptron with enhanced local-input-fault-tolerances." Optics Communications 135, no. 4-6 (1997): 203–6. http://dx.doi.org/10.1016/s0030-4018(96)00659-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Paluzo-Hidalgo, Eduardo, Rocio Gonzalez-Diaz, and Miguel A. Gutiérrez-Naranjo. "Two-hidden-layer feed-forward networks are universal approximators: A constructive approach." Neural Networks 131 (November 2020): 29–36. http://dx.doi.org/10.1016/j.neunet.2020.07.021.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Liu, Cheng-Yi, Chein Chen, Ching-Ter Chang, and Lun-Min Shih. "Single-hidden-layer feed-forward quantum neural network based on Grover learning." Neural Networks 45 (September 2013): 144–50. http://dx.doi.org/10.1016/j.neunet.2013.02.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Luo, X., A. D. Patton, and C. Singh. "Real power transfer capability calculations using multi-layer feed-forward neural networks." IEEE Transactions on Power Systems 15, no. 2 (2000): 903–8. http://dx.doi.org/10.1109/59.867192.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Liqaa, Saadi Mezher. "Hamming neural network application with FPGA device." International Journal of Reconfigurable and Embedded Systems 10, no. 1 (2021): 37–46. https://doi.org/10.11591/ijres.v10.i1.pp37-46.

Full text

Abstract:

The Hamming neural network is a kind of counterfeit neural system that substance of two kinds of layers (feed forward layers and repetitive layer). In this study, two pattern entries are utilization in the binary number. In the first layer, two nerves were utilization as the pure line work. In the subsequent layer, three nerves and a positive line work were utilization. The Hamming Neural system calculation was also implemented in three reproduction strategies (logical gate technique, programming program encryption strategy and momentary square chart technique). In this study in programming of VHDL and FPGA machine was utilization.

APA, Harvard, Vancouver, ISO, and other styles

28

Singh, Ramvir, and Manju Dabas. "Neural Network-Based Prediction of Effective Heat Storage Coefficient of Building Materials." Defect and Diffusion Forum 354 (June 2014): 73–78. http://dx.doi.org/10.4028/www.scientific.net/ddf.354.73.

Full text

Abstract:

In the present paper, we have employed the application of artificial neural networks (ANN) to predict effective heat storage coefficient (HSC) of building materials. First we prepared a database to train and test the models developed here. Two types of architectures from different networks are developed, one with three inputs and the other with four inputs mixed architecture combining an ANN with a theoretical model developed by us previously. These ANN models are built, trained and tested by the feed forward back propagation algorithm, to obtain the effective properties of building materials from the properties of their constituents. Feed forward back propagation neural network structure has been developed, which includes an input layer, a hidden layer and an output layer. The number of neurons in the input layer is equal to the number of input parameters and the number of neurons in the output layer is equal to the output parameters. A good agreement has been found between the predicted values using ANN and the experimental results reported in the literature.

APA, Harvard, Vancouver, ISO, and other styles

29

Qiao, Z. X., Y. Zhou, and Z. Wu. "Turbulent boundary layer under the control of different schemes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2202 (2017): 20170038. http://dx.doi.org/10.1098/rspa.2017.0038.

Full text

Abstract:

This work explores experimentally the control of a turbulent boundary layer over a flat plate based on wall perturbation generated by piezo-ceramic actuators. Different schemes are investigated, including the feed-forward, the feedback, and the combined feed-forward and feedback strategies, with a view to suppressing the near-wall high-speed events and hence reducing skin friction drag. While the strategies may achieve a local maximum drag reduction slightly less than their counterpart of the open-loop control, the corresponding duty cycles are substantially reduced when compared with that of the open-loop control. The results suggest a good potential to cut down the input energy under these control strategies. The fluctuating velocity, spectra, Taylor microscale and mean energy dissipation are measured across the boundary layer with and without control and, based on the measurements, the flow mechanism behind the control is proposed.

APA, Harvard, Vancouver, ISO, and other styles

30

Duguid, Ian, Tiago Branco, Paul Chadderton, Charlotte Arlt, Kate Powell, and Michael Häusser. "Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition." Proceedings of the National Academy of Sciences 112, no. 42 (2015): 13099–104. http://dx.doi.org/10.1073/pnas.1510249112.

Full text

Abstract:

Classical feed-forward inhibition involves an excitation–inhibition sequence that enhances the temporal precision of neuronal responses by narrowing the window for synaptic integration. In the input layer of the cerebellum, feed-forward inhibition is thought to preserve the temporal fidelity of granule cell spikes during mossy fiber stimulation. Although this classical feed-forward inhibitory circuit has been demonstrated in vitro, the extent to which inhibition shapes granule cell sensory responses in vivo remains unresolved. Here we combined whole-cell patch-clamp recordings in vivo and dynamic clamp recordings in vitro to directly assess the impact of Golgi cell inhibition on sensory information transmission in the granule cell layer of the cerebellum. We show that the majority of granule cells in Crus II of the cerebrocerebellum receive sensory-evoked phasic and spillover inhibition prior to mossy fiber excitation. This preceding inhibition reduces granule cell excitability and sensory-evoked spike precision, but enhances sensory response reproducibility across the granule cell population. Our findings suggest that neighboring granule cells and Golgi cells can receive segregated and functionally distinct mossy fiber inputs, enabling Golgi cells to regulate the size and reproducibility of sensory responses.

APA, Harvard, Vancouver, ISO, and other styles

31

Wigati, Ekky Rosita Singgih, Budi Warsito, and Rita Rahmawati. "PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT." Jurnal Gaussian 7, no. 1 (2018): 64–72. http://dx.doi.org/10.14710/j.gauss.v7i1.26636.

Full text

Abstract:

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

APA, Harvard, Vancouver, ISO, and other styles

32

Brajen, Kumar Deka, and Das Pranab. "Isolated Keyword Spotting in Multilingual Environment using ANN and MFCC." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 5–8. https://doi.org/10.35940/ijeat.C6135.049420.

Full text

Abstract:

The performance and analysis of Keyword Spotting system (KWS) are applied when the training and testing in a multilingual environment. This paper exhibits an approach for building up a multilingual KWS framework for Assamese, English and Hindi language dependent on feed-forward neural system. Mel Frequency Cepstral Coefficient (MFCC) has been utilized for highlight extraction which gives a lot of highlight vectors from recorded sound examples. Neural Network backpropagation model is utilized to improve the acknowledgment execution on the recently made multilingual database utilizing the multi-layer feed-forward neural system classifier.

APA, Harvard, Vancouver, ISO, and other styles

33

Singh, Priyanka, Samir Kumar Borgohain, Achintya Kumar Sarkar, Jayendra Kumar, and Lakhan Dev Sharma. "Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection." International Journal of Intelligent Systems 2023 (February 20, 2023): 1–20. http://dx.doi.org/10.1155/2023/9544481.

Full text

Abstract:

The portable executable header (PEH) information is commonly used as a feature for malware detection systems to train and validate machine learning (ML) or deep learning (DL) classifiers. We propose to extract the deep features from the PEH information through hidden layers of a feed-forward deep neural network (FFDNN). The extraction of deep features of hidden layers represents the dataset with a better generalization for malware detection. While feeding the deep feature of one hidden layer to the succeeding layer, the Gaussian error linear unit (GeLU) activation function is applied. The FFDNN is trained with the GeLU activation function using the deep features of individual layers as well as concatenated deep features of all hidden layers. Similarly, the ML classifiers are also trained and validated in with individual layer deep features and concatenated features. Three highly effective ML classifiers, random forest (RF), support vector machine (SVM), and k-nearest neighbour (k-NN) have been investigated. The performance of the proposed model is demonstrated using a statically significant large dataset. The obtained results are interesting and encouraging in terms of classification accuracy. The classification accuracy reaches 99.15% with the internal discriminative deep feature for the proposed FFDNN-ML classifier with the GeLU activation function.

APA, Harvard, Vancouver, ISO, and other styles

34

Koyuncu, Hakan. "Determination of positioning accuracies by using fingerprint localisation and artificial neural networks." Thermal Science 23, Suppl. 1 (2019): 99–111. http://dx.doi.org/10.2298/tsci180912334k.

Full text

Abstract:

Fingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network, multi-layer feed forward neural network, multi-layer back propagation neural network, general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.

APA, Harvard, Vancouver, ISO, and other styles

35

S., Murugan *1. "PERFORMANCE AND COMPARATIVE ANALYSIS OF INDIAN STOCK MARKET DATA USING MULTI LAYER FEED FORWARD NEURAL NETWORK AND FUZZY TIME SERIES MULTI LAYER FEED FORWARD NEURAL NETWORK MODEL WITH TRACKING SIGNAL APPROACH." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 61–69. https://doi.org/10.5281/zenodo.2595802.

Full text

Abstract:

This study, proposes a novel neural network and fuzzy-neural network approach for predicting the closing index of the stock market. It strives to adapt the number of hidden neurons of a Multi Layer Feed Forward Neural Network (MLFFNN) and Fuzzy Time Series Multi Layer Feed Forward Neural Network (FTS-MLFFNN) model. It uses the Tracking Signal (TS) and rejects all models which result in values outside the interval of [-4, +4]. The effectiveness of the proposed approach is verified with one step ahead of Bombay Stock Exchange (BSE100) closing stock index of Indian stock market. This novel approach reduces the over-fitting problem, reduces the neural network structure and improves prediction accuracy. In addition, the result of MLFFNN with TS approach is compared to FTS-MLFFNN with TS approach. The result indicates that the FTS-MLFFNN with TS approach outperforms the MLFFNN with TS approach.

APA, Harvard, Vancouver, ISO, and other styles

36

Qin, Jian-Jun, Maung Htun Oo, Guihe Tao, et al. "Optimization of Operating Conditions in Forward Osmosis for Osmotic Membrane Bioreactor." Open Chemical Engineering Journal 3, no. 1 (2009): 27–32. http://dx.doi.org/10.2174/1874123100903010027.

Full text

Abstract:

Objective of this study was to conduct a baseline study of osmotic membrane bioreactor (OMBR) - optimization of operating conditions in forward osmosis (FO). Experiments were conducted with an FO pilot system. Tap water was used as the feed and NaCl and MgSO4 solutions were used as draw solution. Effects of various operating conditions on flux have been investigated. In addition, pure water permeability of the FO membrane was tested. It was observed that the plant operation could be stablized within 1 h. When the membrane selective layer faced to the feed, a flux of 6.3 lm-2h-1 (LMH) was achieved at 24 atm osmotic pressure and 25 °C and effects of feed velocity and air velocity on flux were not siganificant under the testing conditions due to low external concentration polarization (ECP). However, when the selective layer faced to the draw solution, the flux was enhanced by 64% due to much reduced internal concentration polarization (ICP), the flux sharply increased with an increase in velocity of the draw solution in the laminar flow pattern range due to a countable effect of dilutive external concentration polarization (DECP) and leveled off after the flow pattern became turbulent. NaCl performed much higher efficiency than MgSO4 as an osmotic agent due to a greater solute diffusion coefficient of NaCl.

APA, Harvard, Vancouver, ISO, and other styles

37

Kim, Minseong, and Hyun-Chul Choi. "Total Style Transfer with a Single Feed-Forward Network." Sensors 22, no. 12 (2022): 4612. http://dx.doi.org/10.3390/s22124612.

Full text

Abstract:

The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully transferred into a content image. In this paper, we propose a new style transfer method, named total style transfer, that resolves this limitation by utilizing intra/inter-scale statistics of multi-scaled feature maps without losing the merits of the existing methods. First, we use a more general feature transform layer that employs intra/inter-scale statistics of multi-scaled feature maps and transforms the multi-scaled style of a content image into that of a style image. Secondly, we generate a multi-scaled stylized image by using only a single decoder network with skip-connections, in which multi-scaled features are merged. Finally, we optimize the style loss for the decoder network in the intra/inter-scale statistics of image style. Our improved total style transfer can generate a stylized image with a scale-across style pattern from a pair of content and style images in one forwarding pass. Our method achieved less memory consumption and faster feed-forwarding speed compared with the recent cascade scheme and the lowest style loss among the recent style transfer methods.

APA, Harvard, Vancouver, ISO, and other styles

38

AMRAN, SITI NOR ZULAIKA, and NORIZAN MOHAMED. "FORECASTING ELECTRICITY SUPPLIED IN TURKEY USING HOLT-WINTERS’ MULTIPLICATIVE METHOD AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS." Universiti Malaysia Terengganu Journal of Undergraduate Research 3, no. 3 (2021): 131–42. http://dx.doi.org/10.46754/umtjur.v3i3.225.

Full text

Abstract:

Electricity is one of the most essential necessities in today’s world and has an important role for the development of societies and economics. The need for electricity is expanding continuously due to increasing population, urbanization and industrialization. Hence, the purpose of this study was to develop the best model for forecasting electricity supplied in Turkey by applying the multiplicative Holt-Winters method and multilayer feed-forward neural network model. The monthly electricity supplied in Turkey from January 2000 until December 2019 were obtained from monthly electricity statistics report presented by the International Energy Agency (EIA). The data were divided into two sets comprising in-sample data from January 2000 until December 2015 and out-sample data from January 2016 to December 2019. The multiplicative Holt-Winters was used since the electricity supplied in Turkey exhibit trend and seasonal gave the out-sample forecast of 3.6990. The best multilayer feed forward neural network (MFFNN) model with three input lag variable, one hidden node, one output node, sigmoid transfer function in hidden layer and linear transfer function in output layer gave the out-sample forecast of 2.1483. Hence it can be concluded that, the multilayer feed-forward neural network model is more accurate than multiplicative Holt-Winters method to forecast the electricity supplied in Turkey.

APA, Harvard, Vancouver, ISO, and other styles

39

Kavitha, B., and D. Sarala Thambavani. "Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by quartz/feldspar/wollastonite." RSC Advances 6, no. 7 (2016): 5837–47. http://dx.doi.org/10.1039/c5ra22851d.

Full text

Abstract:

A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr(vi) in aqueous solution using riverbed sand containing quartz/feldspar/wollastonite (QFW) as adsorbent.

APA, Harvard, Vancouver, ISO, and other styles

40

Arslan, Ozkan, and Erkan Zeki Engin. "Noise Robust Voice Activity Detection Based on Multi-Layer Feed-Forward Neural Network." Electrica 19, no. 2 (2019): 91–100. http://dx.doi.org/10.26650/electrica.2019.18042.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Gonzalez, Larry P., and Carlos M. Arnaldo. "Classification of drug-induced behaviors using a multi-layer feed-forward neural network." Computer Methods and Programs in Biomedicine 40, no. 3 (1993): 167–73. http://dx.doi.org/10.1016/0169-2607(93)90054-o.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

., V. Parthasarathy. "A SIMPLIFIED DESIGN OF MULTIPLIER FOR MULTI LAYER FEED FORWARD HARDWARE NEURAL NETWORKS." International Journal of Research in Engineering and Technology 03, no. 24 (2014): 43–47. http://dx.doi.org/10.15623/ijret.2014.0324009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

SIMODA, Fuyuki, Satoshi TAKAMOTO, Toru EGUCHI, and Fuminori OBA. "Bi-criteria Job Shop Scheduling Using a Multi-layer Feed-forward Neural Network." Proceedings of Conference of Chugoku-Shikoku Branch 2002.40 (2002): 477–78. http://dx.doi.org/10.1299/jsmecs.2002.40.477.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Taweewat, Pat, and Chai Wutiwiwatchai. "Musical pitch estimation using a supervised single hidden layer feed-forward neural network." Expert Systems with Applications 40, no. 2 (2013): 575–89. http://dx.doi.org/10.1016/j.eswa.2012.07.063.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

BISRY, AHMAD, CECEP MUHAMAD SIDIK RAMDANI, and SITI YULIYANTI. "Pengujian Parameter Algoritma Genetika dan Feed-Forward Neural Networks pada Permainan Ular Klasik." MIND Journal 9, no. 2 (2024): 135–52. https://doi.org/10.26760/mindjournal.v9i2.135-152.

Full text

Abstract:

AbstrakKonfigurasi parameter yang tepat sangat penting untuk memaksimalkan kinerja dari sebuah algoritma. Algoritma genetika dan neural networks memerlukan pemilihan parameter yang sesuai dalam penggunaannya. Pada permainan ular, performa diukur dari score dan efisiensi runtime. Penelitian ini menguji parameter untuk menemukan konfigurasi optimal bagi kedua algoritma. Permainan ular digunakan sebagai model eksperimen karena metrik kinerja yang jelas, seperti score yang didapat dan beberapa rintangan yang ada. Sebanyak 60 eksperimen dilakukan untuk membandingkan jumlah generasi dan populasi, mutation chance, dan jumlah neuron pada hidden layer. Hasil penelitian menunjukkan konfigurasi dengan generasi lebih besar dari populasi adalah yang paling optimal, menghasilkan score setara dengan generasi dan populasi yang sama besar, namun dengan runtime lebih efisien. Mutation chance 0.1% merupakan yang terbaik dibandingkan dengan 0.2% sampai 0.5%. Selain itu, hidden layer dengan 16 neuron lebih efisien dibandingkan 24 neuron, baik dari segi score maupun runtime.Kata kunci: Algoritma genetika, neural networks, Permainan ular klasikAbstract Appropriate parameter configuration is crucial to maximizing algorithm performance. Genetic algorithms and neural networks require careful parameter selection. In the game of Snake, performance is measured by score and runtime efficiency. This research tests parameters to find optimal configurations for both algorithms. Snake serves as an experimental model due to clear performance metrics such as score and various obstacles. Sixty experiments compare generation and population sizes, mutation chances, and neuron counts in hidden layers. Findings indicate that configurations with larger generations than populations are optimal, yielding scores similar to equal-sized generations and populations but with more efficient runtime. A 0.1% mutation chance outperforms rates of 0.2% to 0.5%. A hidden layer with 16 neurons proves more efficient than 24 neurons in both score and runtime aspects.Keywords: Genetic algorithm, neural networks, classic snake game

APA, Harvard, Vancouver, ISO, and other styles

46

Abdullah, Saleem, Alaa O. Almagrabi, and Nawab Ali. "A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks." Mathematics 11, no. 13 (2023): 2972. http://dx.doi.org/10.3390/math11132972.

Full text

Abstract:

A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination and interrelationship of neurons and nodes with each other facilitate the transmission of information. An ANN has a feed-forward neural network. The neurons are arranged in layers, and each layer performs a particular calculation on the incoming data. Up until the output layer, which generates the network’s ultimate output, is reached, each layer’s output is transmitted as an input to the subsequent layer. A feed-forward neural network (FFNN) is a method for finding the output of expert information. In this research, we expand upon the concept of fuzzy neural network systems and introduce feed-forward double-hierarchy linguistic neural network systems (FFDHLNNS) using Yager–Dombi aggregation operators. We also discuss the desirable properties of Yager–Dombi aggregation operators. Moreover, we describe double-hierarchy linguistic term sets (DHLTSs) and discuss the score function of DHLTSs and the distance between any two double-hierarchy linguistic term elements (DHLTEs). Here, we discuss different approaches to choosing a novel water purification technique on a commercial scale, as well as some variables influencing these approaches. We apply a feed-forward double-hierarchy linguistic neural network (FFDHLNN) to select the best method for water purification. Moreover, we use the extended version of the Technique for Order Preference by Similarity to Ideal Solution (extended TOPSIS) method and the grey relational analysis (GRA) method for the verification of our suggested approach. Remarkably, both approaches yield almost the same results as those obtained using our proposed method. The proposed models were compared with other existing models of decision support systems, and the comparison demonstrated that the proposed models are feasible and valid decision support systems. The proposed technique is more reliable and accurate for the selection of large-scale water purification methods.

APA, Harvard, Vancouver, ISO, and other styles

47

Srisaeng, Panarat, Glenn S. Baxter, and Graham Wild. "FORECASTING DEMAND FOR LOW COST CARRIERS IN AUSTRALIA USING AN ARTIFICIAL NEURAL NETWORK APPROACH." Aviation 19, no. 2 (2015): 90–103. http://dx.doi.org/10.3846/16487788.2015.1054157.

Full text

Abstract:

This study focuses on predicting Australia‘s low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia‘s real GDP, real GDP per capita, air fares, Australia‘s population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

APA, Harvard, Vancouver, ISO, and other styles

48

Medini, Chaitanya, Bipin Nair, Egidio D'Angelo, Giovanni Naldi, and Shyam Diwakar. "Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles." Computational Intelligence and Neuroscience 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/359529.

Full text

Abstract:

The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.

APA, Harvard, Vancouver, ISO, and other styles

49

Lyakhomsky, Alexander, and Andrei Shadrin. "POWER CONSUMPTION FORECASTING BASED ON FULLY CONNECTED FEED-FORWARD NEURAL NETWORKS." Electrical and data processing facilities and systems 18, no. 1 (2022): 107–13. http://dx.doi.org/10.17122/1999-5458-2022-18-1-107-113.

Full text

Abstract:

The relevance The relevance of electricity consumption forecasting on the basis of fully connected feed-forward neural networks (FNN) to improve the validity of applications for electricity is considered. Aim of research Synthesis of predictive model of electricity consumption in the form of four-layer fully connected feed-forward neural network, linking the volume of production and the predicted electricity consumption is performed. Research methods The algorithm of the predictive model development includes: formation and initial statistical processing of initial data; determination of FNN structure hyperparameters — total number of layers, number of neurons in layers, activation function, training rate coefficient; selection of optimization method; training, checking model adequacy. Results The analytical expression for the description of the forecast model based on FNN is given. The synthesized forecast model makes it possible to increase the validity of electric power applications of enterprises.

APA, Harvard, Vancouver, ISO, and other styles

50

Swadlow, Harvey A. "Thalamocortical control of feed–forward inhibition in awake somatosensory ‘barrel’ cortex." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1428 (2002): 1717–27. http://dx.doi.org/10.1098/rstb.2002.1156.

Full text

Abstract:

Intracortical inhibition plays a role in shaping sensory cortical receptive fields and is mediated by both feed–forward and feedback mechanisms. Feed–forward inhibition is the faster of the two processes, being generated by inhibitory interneurons driven by monosynaptic thalamocortical (TC) input. In principle, feed–forward inhibition can prevent targeted cortical neurons from ever reaching threshold when TC input is weak. To do so, however, inhibitory interneurons must respond to TC input at low thresholds and generate spikes very quickly. A powerful feed–forward inhibition would sharpen the tuning characteristics of targeted cortical neurons, and interneurons with sensitive and broadly tuned receptive fields could mediate this process. Suspected inhibitory interneurons (SINs) with precisely these properties are found in layer 4 of the somatosensory (S1) ‘barrel’ cortex of rodents and rabbits. These interneurons lack the directional selectivity seen in most cortical spiny neurons and in ventrobasal TC afferents, but are much more sensitive than cortical spiny neurons to low–amplitude whisker displacements. This paper is concerned with the activation of S1 SINs by TC impulses, and with the consequences of this activation. Multiple TC neurons and multiple S1 SINs were simultaneously studied in awake rabbits, and cross–correlation methods were used to examine functional connectivity. The results demonstrate a potent, temporally precise, dynamic and highly convergent/divergent functional input from ventrobasal TC neurons to SINs of the topographically aligned S1 barrel. Whereas the extensive pooling of convergent TC inputs onto SINs generates sensitive and broadly tuned inhibitory receptive fields, the potent TC divergence onto many SINs generates sharply synchronous activity among these elements. This TC feed–forward inhibitory network is well suited to provide a fast, potent, sensitive and broadly tuned inhibition of targeted spiny neurons that will suppress spike generation following all but the most optimal feed–forward excitatory inputs.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Feed forward layer'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles