scholarly journals Using Attenuation Coefficient Generating Function in Parallel Execution of Neural Networks for Solving SAT

2005 ◽  
Vol 9 (2) ◽  
pp. 121-126 ◽  
Author(s):  
Kairong Zhang ◽  
◽  
Masahiro Nagamatu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Probability Density Function ◽  
Attenuation Coefficient ◽  
Parallel Execution ◽  
Experimental Results ◽  
Attenuation Coefficients ◽  
Execution Speed ◽  
Speedup Ratio ◽  
Analog Neural Network

The satisfiability problem (SAT) is one of the most basic and important problems in computer science. We have proposed a recurrent analog neural network called Lagrange Programming neural network with Polarized High-order connections (LPPH) for the SAT, together with a method of parallel execution of LPPH. Experimental results demonstrate a high speedup ratio. Furthermore this method is very easy to realize by hardware. LPPH dynamics has an important parameter, the attenuation coefficient, known to strongly affect LPPH execution speed, but determining a good value of attenuation coefficient is difficult. Experimental results show that the parallel execution reduces this difficulty. In this paper we propose a method to assign different values of attenuation coefficients to LPPHs used in the parallel execution. The values are generated uniformly randomly or randomly using a probability density function.

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

scholarly journals PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Formation Process ◽  
Robot Control ◽  
Experimental Results ◽  
The Road ◽  
Mobile Robot Control ◽  
Different Parts ◽  
Training Sets

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.

Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

2013 ◽  
Vol 371 ◽  
pp. 812-816 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Results ◽  
Work Place ◽  
Feed Forward Neural Network ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
Human Effort ◽  
Hidden Layer

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

scholarly journals Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 39
Author(s):  
Hongpeng Liao ◽  
Jianwu Xu ◽  
Zhuliang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Experimental Results ◽  
Redundant Information ◽  
Promising Tool ◽  
Information Bottleneck

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

scholarly journals Modeling the restoration of biological and biotechnical systems using hardware analog and software artificial neural networks

2018 ◽  
Vol 80 (2) ◽  
pp. 86-92
Author(s):  
Ya. A. Turovskii ◽  
E. V. Bogatikov ◽  
S. G. Tikhomirov ◽  
A. A. Adamenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Analog Model ◽  
Recovery Method ◽  
Evolutionary Method ◽  
Artificial Neural ◽  
Self Learning ◽  
Analog Neural Network ◽  
Data Banks

A hardware analog model of an artificial neural network was developed, based on a specially trained software artificial neural network, for modeling the process of recovering damaged biological and biotechnical systems using neurochips based on the evolutionary method of training. A series of 12 computational experiments on the restoration of a damaged hardware analog artificial neural network with the help of a software artificial neural network was carried out. To restore a damaged network, an evolutionary approach is used. In most cases, it is possible to restore a damaged hardware analog neural network to 100% accuracy. The obtained results confirm the efficiency of the proposed approach in the framework of modeling the restoration of damaged biological and biotechnical systems using a neurochipon the basis of the evolutionary method using the "isolation" mechanism. The proposed recovery method opens up prospects for such areas as neuroprosthetics, self-learning and self-adapting systems; reverse-engineering; restoration of damaged data banks, image restoration; decision making and management, and so on.

scholarly journals On the prediction of solar activity using different neural network models

1996 ◽  
Vol 14 (1) ◽  
pp. 20-26 ◽  
Author(s):  
F. Fessant ◽  
S. Bengio ◽  
D. Collobert
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Solar Activity ◽  
Time Series Prediction ◽  
Network Models ◽  
Accurate Prediction ◽  
Experimental Results ◽  
Neural Network Models ◽  
Telecommunication Companies

Abstract. Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters rely strongly on solar activity. In this paper, we analyze the use of neural networks for sunspot time series prediction. Three types of models are tested and experimental results are reported for a particular sunspot time series: the IR5 index.

scholarly journals Vehicle Classification Based on FBG Sensor Arrays Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4472 ◽  
Author(s):  
Michal Frniak ◽  
Miroslav Markovic ◽  
Patrik Kamencay ◽  
Jozef Dubovan ◽  
Miroslav Benco ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensor Arrays ◽  
Experimental Results ◽  
Vehicle Classification ◽  
Experimental Platform ◽  
Television Camera ◽  
Fbg Sensor ◽  
Bragg Grating Sensor

This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.

scholarly journals Improving Neural Network Verification through Spurious Region Guided Refinement

2021 ◽  
pp. 389-408
Author(s):  
Pengfei Yang ◽  
Renjue Li ◽  
Jianlin Li ◽  
Cheng-Chao Huang ◽  
Jingyi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Programming ◽  
Experimental Results ◽  
Abstraction Refinement ◽  
Robustness Property ◽  
The Core ◽  
Programming Techniques ◽  
Core Idea ◽  
Network Verification

AbstractWe propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques. With the new bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.

scholarly journals Case Studies on Neural Networks for Recognition in Biometric Identity Problem

2021 ◽  
Vol 25 (1) ◽  
pp. 5-12
Author(s):  
Zhengwen Shen ◽  
◽  
Jun Wang ◽  
Zaiyu Pan ◽  
Kai Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Case Studies ◽  
The Self ◽  
Experimental Results ◽  
Identity Problem ◽  
Vein Recognition

Hand-dorsa vein recognition using a convolutional neural network is presented. Our network contains five convolutional layers and three full connected layers, which have high recognition and more robust. The experimental results on the self-established database with the proposed CNN achieves 98.02% in training part and 97.65% in testing part, which demonstrates the effectiveness of the proposed CNN.

scholarly journals Evolving Neural Networks for Static Single-Position Automated Trading

2008 ◽  
Vol 2008 ◽  
pp. 1-17 ◽  
Author(s):  
Antonia Azzini ◽  
Andrea G. B. Tettamanzi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Input Data ◽  
Trading Strategy ◽  
Experimental Results ◽  
Financial Instrument ◽  
The Neural Network ◽  
Trading Agent ◽  
Selection Of

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.

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