Application of Neural Network in Bioactive Prediction of Inhibitor of Thymidylic Acid-Based Synthetase

2014 ◽  
Vol 937 ◽  
pp. 308-312
Author(s):  
Xi Hua Du ◽  
Xiao Hui Wang
Keyword(s):  
Neural Network ◽  
Biological Activity ◽  
Molecular Design ◽  
Structural Parameters ◽  
Regression Method ◽  
New Drugs ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Electrical State ◽  
Neural Network Input

Based on the molecular topology information and adjacency matrix, the 38 electrical state indices of molecules of inhibitor of thymidylic acid-based synthetase as five-membered heterocyclic pyrimidine derivatives were calculated to provide theoretical basis for molecular design of new drugs. By using variable regression method, the best subset of structural parameters ofE1,E2,E7,E16andE31were optimized. When the five structural parameters were used as the BP neural network input neurons and the neural network structure of 5:3:1 was used, an ideal prediction model of biological activity was obtained. Its total correlation coefficientrand average relative error were 0.972 and 2.13%, respectively. The result showed that the biological activity andE1,E2,E7,E16andE31have a good non-linear relationship with the biological activity, and the results predicted by neural networks was better than that by multiple regression method. The test proved that the model had good robust and predictive capabilities. Our research would provide theoretical guidance for the development of new drugs of inhibitor of thymidylic acid-based synthetase with efficient and low toxicity.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

scholarly journals CHOOSING A NEURAL NETWORK STRUCTURE FOR SIGNAL PROCESSING AS AN EXPERIMENT PLANNING

2021 ◽  
pp. 123-132
Author(s):  
Leonid A. Slavutskii ◽  
Elena V. Slavutskaya
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Electric Power Industry ◽  
Planning Theory ◽  
Experiment Planning ◽  
Harmonic Amplitude ◽  
The Neural Network ◽  
Nonlinear Distortions ◽  
Neural Network Structure

The paper is devoted to the use of artificial neural networks for signal processing in electrical engineering and electric power industry. Direct propagation neural network (perceptron) is considered as an object in the theory of experiment planning. The variants of the neural network structure empirical choice, the quality criteria of its training and testing are analyzed. It is shown that the perceptron structure choice, the training sample, and the training algorithms require planning. Variables and parameters of neuro algorithm that can act as factors, state parameters, and disturbing influences in the framework of the experimental planning theory are discussed. The proposed approach is demonstrated by the example of neural network analysis of the industrial frequency signal of 50 Hz nonlinear distortions. The possibility of using an elementary perceptron with one hidden layer and a minimum number of neurons to correct the transformer saturation current is analyzed. The conditions under which the neuro algorithm allows one to restore the values of the main harmonic amplitude, frequency and phase with an error of no more than one percent are revealed. The signal processing in a «sliding window» with a duration of a fraction of the fundamental frequency period is proposed, and the neuro algorithm accuracy characteristics are estimated. The possibility to automate the neural network structure choosing for signal processing is discussed.

scholarly journals Moisture Estimation in Cabinet Dryer with Thin-Layer Relationships Using Genetic Algorithm and Neural Network

2019 ◽  
Author(s):  
Behzad Maleki ◽  
Mahyar Ghazvini ◽  
Mohammad Hossein Ahmadi ◽  
Heydar Maddah ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thin Layer ◽  
Experimental Models ◽  
Moisture Loss ◽  
Squared Error ◽  
The Neural Network ◽  
Quality Product ◽  
Neural Network Structure ◽  
Moisture Estimation

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

scholarly journals INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS

2018 ◽  
pp. 47-54
Author(s):  
Vitalii Lysenko ◽  
Oleksiy Opryshko ◽  
Dmytro Komarchuk ◽  
Natalia Pasichnyk ◽  
Natalya Zaets ◽  
...  
Keyword(s):  
Neural Network ◽  
Nitrogen Nutrition ◽  
Information Support ◽  
Statistical Characteristics ◽  
Intermediate Layers ◽  
The Neural Network ◽  
The Matrix ◽  
Data Volume ◽  
Linear Elements ◽  
Neural Network Input

The article addresses issues on application of unmanned aerial vehicles (UAV) to monitor nitrogen nutrition through the example of wheat plants. The optical spectral range can be used to monitor exploitation of the UAV. It is recommended to develop specialized spectral indices for such equipment. The article provides calibration curves for nitrogen nutrition monitoring. In the created neural networks, the linear model is represented as a network without intermediate layers, which in the output layer contains only linear elements, the weight corresponds to the elements of the matrix, and the thresholds are the components of the shear vector. During the operation, the neural network actually multiplies the vector of inputs into the matrix of scales, and then adds a vector of displacement to the resulting vector. Results of the research show how to create the specialized RPVI adapted to technological capabilities of UAVs. It has been experimentally proved that input parameters that describe the state of agricultural plantations are regularly distributed. The average statistical characteristics for additive color RGB model is advisable to be the neural network input instead of large sample data volume.

scholarly journals Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiali Li ◽  
Zhengyu Lv ◽  
Bo Liu ◽  
Licheng Wu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Computer Game ◽  
Feature Learning ◽  
Superior Performance ◽  
Computing Power ◽  
Unit Structure ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Hidden Layer

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use.

A Neural Network Structure Decomposition Based on Pruning and its Visualization Method

2013 ◽  
Vol 17 (3) ◽  
pp. 443-449
Author(s):  
Atsushi Shibata ◽  
◽  
Jiajun Lu ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Structure ◽  
Robot Motion ◽  
Network Structures ◽  
Visualization Method ◽  
Pruning Method ◽  
The Neural Network ◽  
Structure Decomposition ◽  
Neural Network Structure

To decompose neural network structures for composite tasks, a pruning method and its visualization method are proposed. Visualization by placing the neurons on a 2D plane clarifies the structure related to each composited task. Experiments on a composite task using two tasks from a UCI dataset show that the neural network of the composite task contains more than 80% of neurons. The proposed methods target the transfer learning of robot motion, and results of an adaptation experiments are also referred.

GA-BP Neural Network Based Tire Noise Prediction

2012 ◽  
Vol 443-444 ◽  
pp. 65-70 ◽  
Author(s):  
Yong Che ◽  
Wang Xin Xiao ◽  
Li Jun Chen ◽  
Zhi Chu Huang
Keyword(s):  
Neural Network ◽  
Optimal Solution ◽  
Noise Prediction ◽  
Bp Network ◽  
Tire Noise ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
Neural Network Structure ◽  
Trained Neural Network ◽  
Set Up

According to the complexity and the highly nonlinear characteristics of the tire sound, various parameters affecting tire noise were analyzed. By employing neural network a new method of tire noise prediction was proposed. Combining BP neural networks with genetic algorithms the noise prediction model was set up. In order to effectively predict tire noise, the neural network structure was designed and the input and output parameters of the network were determined. The genetic algorithm was added to the BP network in order to optimize initial weights and search out the optimal solution of the network. Applying laboratory drum method large amounts of tire noise test samples were obtained to train the BP network. Trained neural network can accurately predict tire noise in range of typical frequency bands. The results show that precision of this method is sufficient and the prediction effect is better.

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