scholarly journals Air Target Threat Assessment Based on Improved Moth Flame Optimization-Gray Neural Network Model

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Longfei Yue ◽  
Rennong Yang ◽  
Jialiang Zuo ◽  
Hao Luo ◽  
Qiuliang Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Threat Assessment ◽  
Learning Performance ◽  
Air Defense ◽  
Powerful Learning ◽  
Proposed Model ◽  
Time Requirements ◽  
Simulation Results

Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment.

scholarly journals Neural network model for multimodal recognition of human aggression

2020 ◽  
pp. 132-149
Author(s):  
М.Ю. Уздяев
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Space ◽  
Smart Environments ◽  
High Fidelity ◽  
Cyberphysical Systems ◽  
Human Aggression ◽  
Proposed Model ◽  
User Actions

Увеличение количества пользователей социокиберфизических систем, умных пространств, систем интернета вещей актуализирует проблему выявления деструктивных действий пользователей, таких как агрессия. При этом, деструктивные действия пользователей могут быть представлены в различных модальностях: двигательная активность тела, сопутствующее выражение лица, невербальное речевое поведение, вербальное речевое поведение. В статье рассматривается нейросетевая модель многомодального распознавания человеческой агрессии, основанная на построении промежуточного признакового пространства, инвариантного виду обрабатываемой модальности. Предлагаемая модель позволяет распознавать с высокой точностью агрессию в условиях отсутствия или недостатка информации какой-либо модальности. Экспериментальное исследование показало 81:8% верных распознаваний на наборе данных IEMOCAP. Также приводятся результаты экспериментов распознавания агрессии на наборе данных IEMOCAP для 15 различных сочетаний обозначенных выше модальностей. Growing user base of socio-cyberphysical systems, smart environments, IoT (Internet of Things) systems actualizes the problem of revealing of destructive user actions, such as various acts of aggression. Thereby destructive user actions can be represented in different modalities: locomotion, facial expression, associated with it, non-verbal speech behavior, verbal speech behavior. This paper considers a neural network model of multi-modal recognition of human aggression, based on the establishment of an intermediate feature space, invariant to the actual modality, being processed. The proposed model ensures high-fidelity aggression recognition in the cases when data on certain modality are scarce or lacking. Experimental research showed 81.8% correct recognition instances on the IEMOCAP dataset. Also, experimental results are given concerning aggression recognition on the IEMOCAP dataset for 15 different combinations of the modalities, outlined above.

scholarly journals A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 273
Author(s):  
Ioannis E. Livieris ◽  
Spiros D. Dafnis ◽  
George K. Papadopoulos ◽  
Dionissios P. Kalivas
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Computational Cost ◽  
Economic Benefits ◽  
Mixed Data ◽  
Cotton Production ◽  
Proposed Model ◽  
Multiple Input ◽  
Fully Connected

Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.

scholarly journals An Efficient Neural Network for Shape from Focus with Weight Passing Method

2018 ◽  
Vol 8 (9) ◽  
pp. 1648 ◽  
Author(s):  
Hyo-Jong Kim ◽  
Muhammad Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Image Sequences ◽  
Depth Maps ◽  
The Neural Network ◽  
Proposed Model ◽  
Real Objects ◽  
Shape From Focus

In this paper, we suggest an efficient neural network model for shape from focus along with weight passing (WP) method. The neural network model is simplified by reducing the input data dimensions and eliminating the redundancies in the conventional model. It helps for decreasing computational complexity without compromising on accuracy. In order to increase the convergence rate and efficiency, WP method is suggested. It selects appropriate initial weights for the first pixel randomly from the neighborhood of the reference depth and it chooses the initial weights for the next pixel by passing the updated weights from the present pixel. WP method not only expedites the convergence rate, but also is effective in avoiding the local minimization problem. Moreover, this proposed method may also be applied to neural networks with diverse configurations for better depth maps. The proposed system is evaluated using image sequences of synthetic and real objects. Experimental results demonstrate that the proposed model is considerably efficient and is able to improve the convergence rate significantly while the accuracy is comparable with the existing systems.

Hierarchical Function Approximation with a Neural Network Model

2012 ◽  
pp. 466-479
Author(s):  
Luis F. de Mingo ◽  
Nuria Gómez ◽  
Fernando Arroyo ◽  
Juan Castellanos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Polynomial Equation ◽  
Neural System ◽  
Input Pattern ◽  
Activation Functions ◽  
Proposed Model ◽  
Generate Polynomial

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation.

A Homotopic Algorithm of NN and its Application for the End-Point Prediction Model of BOF Process

2014 ◽  
Vol 513-517 ◽  
pp. 431-434
Author(s):  
Ming Xia Feng ◽  
Ren Chen ◽  
Qiang Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Homotopy Theory ◽  
Smelting Process ◽  
Original Result ◽  
New Model ◽  
End Point ◽  
Proposed Model ◽  
Gradient Descent Algorithm

A Homotopic BI neural network model is developed by combining the homotopy theory and the BI neural network model, to improve the defects of the steepest gradient descent algorithm itself, such as low speed converging and liable to be trapped in local minimum. The end-point carbon content and temperature of molten steel in BOF smelting process is predicted by the proposed model and the original. Result shows that the precision of new model is improved significantly. The hit rates are increased by about 5% and 10%, and the forecasting residuals have decreased 16.31% and 8.67% than the conventional ones, respectively. Also, the calculation time of the new model is 10% shorter than BI model.

Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system

2018 ◽  
Vol 29 (7) ◽  
pp. 1073-1097 ◽  
Author(s):  
Gurinderpal Singh ◽  
VK Jain ◽  
Amanpreet Singh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Error Function ◽  
Learning Performance ◽  
Heating Efficiency ◽  
Heating Model ◽  
The Impact ◽  
Self Adaptive

The photovoltaic thermal greenhouse system highly supports the production of biogas. The system’s prime advantage is biogas heating and crop drying through varied directions of air flow. Further, it diminishes the upward loss of the system. This paper aims to model a practical greenhouse system for obtaining the precise estimation of the heating efficiency, given by the solar radiance. The simulation model adopts the self-adaptive firefly neural network model that applies on known experimental data. Therefore, the error function between the model outcome and the experimental outcome is substantially minimized. The performance analysis involves an effective comparative study on the root mean square error between the adopted self-adaptive firefly neural network model and the conventional models such as Levenberg–Marquardt neural network and firefly neural network. Later, the impact of self-adaptiveness, FF update and learning performance on attaining the knowledge regarding the characteristics of SAFF algorithm is analysed to yield better performance.

scholarly journals Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Jinying Kong ◽  
Yating Yang ◽  
Lei Wang ◽  
Xi Zhou ◽  
Tonghai Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Statistical Machine Translation ◽  
Learning Problem ◽  
Proposed Model ◽  
Discriminative Model

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

scholarly journals PROPOSING A NEURAL NETWORK MODEL TO PREDICT TIME AND COST CLAIMS IN CONSTRUCTION PROJECTS

2016 ◽  
Vol 22 (7) ◽  
pp. 967-978 ◽  
Author(s):  
Vahidreza YOUSEFI ◽  
Siamak HAJI YAKHCHALI ◽  
Mostafa KHANZADI ◽  
Ehsan MEHRABANFAR ◽  
Jonas ŠAPARAUSKAS
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Construction Industry ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Management ◽  
Construction Projects ◽  
New Model ◽  
Proposed Model ◽  
The Cost

Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry.

scholarly journals Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model

2015 ◽  
Vol 113 (7) ◽  
pp. 2360-2375 ◽  
Author(s):  
Stephanie Westendorff ◽  
Shenbing Kuang ◽  
Bahareh Taghizadeh ◽  
Opher Donchin ◽  
Alexander Gail
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Visual Target ◽  
Hand Position ◽  
Prediction Errors ◽  
Visually Guided ◽  
Target Error ◽  
Adaptation Mechanisms ◽  
Simulation Results

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement (“jump”) consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation.

scholarly journals Application on Online Process Learning Evaluation Based on Optimal Discrete Hopfield Neural Network and Entropy Weight TOPSIS Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chuanshuang Hu ◽  
Yongmei Ma ◽  
Ting Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Learning Performance ◽  
Topsis Method ◽  
Entropy Weight ◽  
Development Education ◽  
Learning Evaluation ◽  
Process Learning

Sustainable development education respects differences and encourages different assessment methods to evaluate students. During the epidemic, many colleges’ examinations changed from offline to online. How to fully consider students’ process learning status and make a reasonable evaluation of students is worthy of research. Based on the process learning data of a course in a university in China, this study establishes a discrete Hopfield neural network model to classify the test samples. In the process of modelling, the grey correlation analysis method is used to optimize the elements affecting students’ comprehensive evaluation index, and it solves the problem of failure of the model due to the large gap between the factors in the traditional discrete Hopfield neural network model. Then, the entropy right TOPSIS method is used to rank samples with the same evaluation grade. Teachers can objectively evaluate each student’s process learning performance according to the ranking results. Finally, the article compares and analyzes the evaluation results of various different methods. The analysis results believe that the optimized discrete Hopfield neural network is feasible in the process learning evaluation, and the model evaluation results are more objective and comprehensive.

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