scholarly journals A Nonpenalty Neurodynamic Model for Complex-Variable Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bao Liu ◽  
Xuehui Mei ◽  
Haijun Jiang ◽  
Lijun Wu
Keyword(s):  
Neural Network ◽  
Differential Inclusion ◽  
Network Model ◽  
Neural Network Model ◽  
Complex Variable ◽  
Optimization Model ◽  
Optimization Problems ◽  
Numerical Examples ◽  
Practical Applications ◽  
Proposed Model

In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.

Effects of the chaotic noise on the performance of a neural network model for optimization problems

1995 ◽  
Vol 51 (4) ◽  
pp. R2693-R2696 ◽  
Author(s):  
Yoshinori Hayakawa ◽  
Atsushi Marumoto ◽  
Yasuji Sawada
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Problems

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.

INDUCING DYNAMIC RULES OF NATIONS' COMPETITIVENESS FROM 2001–2005 MCI-WCY

2009 ◽  
Vol 08 (03) ◽  
pp. 549-580 ◽  
Author(s):  
HAN-LIN LI ◽  
YU-CHIEN KO
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Model ◽  
Business Decision ◽  
The World

A nation's competitiveness has become more and more important in forming government strategy and business decision making. This study proposes an optimization model, instead of regression model or neural network model, to induce rules for dynamic nations' competitiveness based on the Major Competitiveness Indicators of the World Competitiveness Yearbook. Fourteen attributes are used to form the dynamic rules expressed in "IF…THEN…" forms. According to the induced rules, the strategic implications are suggested for various groups of nations to improve or to sustain their competitiveness.

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.

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.

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