Polynomial dissipativity of proportional delayed BAM neural networks

2020 ◽  
Vol 13 (06) ◽  
pp. 2050050 ◽  
Author(s):  
Lin Xing ◽  
Liqun Zhou
Keyword(s):  
Neural Networks ◽  
Simulation Experiment ◽  
Lyapunov Functionals ◽  
Bam Neural Networks ◽  
Close Attention ◽  
Halanay Inequality ◽  
Attracting Set ◽  
The Relationship ◽  
Theoretical Condition ◽  
Generalized Halanay Inequality

This paper pays close attention to the global polynomial dissipativity (GPD) for proportional delayed BAM neural networks (PDBAMNNs). The global exponential dissipativity (GED) and the global dissipativity (GD) are also talked about. Under the help of novel Lyapunov functionals and a generalized Halanay inequality, a set of dissipative criteria for such systems are led out, together with the global polynomial attracting set (GPAS) and the global attracting set (GAS). Further, the relationship among GPD, GED and GD is unveiled. Finally, a proposed theoretical condition is validated through a simulation experiment.

Global dissipativity of non-autonomous BAM neural networks with mixed time-varying delays and discontinuous activations

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Meng Yan ◽  
Minghui Jiang ◽  
Kaifang Fei
Keyword(s):  
Neural Networks ◽  
Functional Differential Equations ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Filippov Solution ◽  
Discontinuous Activations ◽  
Functional Differential ◽  
Globally Attractive ◽  
Functional Differential Inclusions ◽  
Generalized Halanay Inequality

Abstract In this paper, we investigate the dissipativity of a class of BAM neural networks with both time-varying and distributed delays, as well as discontinuous activations. First, the concept of the Filippov solution is extended to functional differential equations with discontinuous right-hand sides via functional differential inclusions. Then, by constructing Lyapunov functional and employing a generalized Halanay inequality, several sufficient easy-to-test conditions are successfully obtained to guarantee the global dissipativity of the Filippov solution of the considered system. The derived results extend and improve some previous publications on conventional BAM neural networks. Meanwhile, the estimations of the positive invariant and globally attractive set are given. Finally, numerical simulations are provided to demonstrate the effectiveness of our proposed results.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Application of intelligent orienteering based on Internet of things

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Donghui Zhang ◽  
Ruijie Liu
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Emergency Services ◽  
Simulation Experiment ◽  
Experimental Studies ◽  
Signal Stability ◽  
Anchor Nodes ◽  
Set Up ◽  
The Relationship ◽  
The Internet Of Things

Abstract Orienteering has gradually changed from a professional sport to a civilian sport. Especially in recent years, orienteering has been widely popularized. Many colleges and universities in China have also set up this course. With the improvement of people’s living conditions, orienteering has really become a leisure sport in modern people’s life. The reduced difficulty of sports enables more people to participate, but it also exposes a series of problems. As the existing positioning technology is relatively backward, the progress in personnel tracking, emergency services, and other aspects is slow. To solve these problems, a new intelligent orienteering application system is developed based on the Internet of things. ZigBee network architecture is adopted in the system. ZigBee is the mainstream scheme in the current wireless sensor network technology, which has many advantages such as convenient carrying, low power consumption, and signal stability. Due to the complex communication environment in mobile signal, the collected information is processed by signal amplification and signal anti-interference technology. By adding anti-interference devices, video isolators and other devices, the signal is guaranteed to the maximum extent. In order to verify the actual effect of this system, through a number of experimental studies including the relationship between error and traffic radius and the relationship between coverage and the number of anchor nodes, the data shows that the scheme studied in this paper has a greater improvement in comprehensive performance than the traditional scheme, significantly improving the accuracy and coverage. Especially the coverage is close to 100% in the simulation experiment. This research has achieved good results and can be widely used in orienteering training and competition.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

Global dissipativity and exponential synchronization of mixed time-varying delays neural networks with discontinuous activations

2020 ◽  
Vol 21 (7-8) ◽  
pp. 693-704 ◽  
Author(s):  
Kaifang Fei ◽  
Minghui Jiang ◽  
Meng Yan ◽  
Weizhen Liu
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Exponential Synchronization ◽  
Matrix Measure ◽  
Analysis Techniques ◽  
Discontinuous Activations ◽  
Inclusion Theory ◽  
Generalized Halanay Inequality ◽  
New Criteria ◽  
Theoretical Results

AbstractIn this paper, the matters of dissipativity and synchronization for non-autonomous Hopfield neural networks with discontinuous activations are investigated. Firstly, under the framework of extending Filippov differential inclusion theory, several effective new criteria are derived. The global dissipativity of Filippov solution to neural networks is proved by using generalized Halanay inequality and matrix measure method. Secondly, the global exponential synchronization of the addressed network drive system and the response system is realized by utilizing inequality and some analysis techniques and designing the discontinuous state feedback controller. Finally, several numerical examples are given to verify the validity of the theoretical results.

Dream Bizarreness as the Cognitive Correlate of Altered Neuronal Behavior in REM Sleep

1989 ◽  
Vol 1 (3) ◽  
pp. 201-222 ◽  
Author(s):  
Adam N. Mamelak ◽  
J. Allan Hobson
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Rem Sleep ◽  
Neuronal Dynamics ◽  
Neurophysiological Model ◽  
The Relationship ◽  
Cognitive Feature ◽  
The Brain ◽  
Cognitive Correlate ◽  
Raphe Neurons

Bizarreness is a cognitive feature common to REM sleep dreams, which can be easily measured. Because bizarreness is highly specific to dreaming, we propose that it is most likely brought about by changes in neuronal activity that are specific to REM sleep. At the level of the dream plot, bizarreness can be defined as either discontinuity or incongruity. In addition, the dreamer's thoughts about the plot may be logically deficient. We propose that dream bizarreness is the cognitive concomitant of two kinds of changes in neuronal dynamics during REM sleep. One is the disinhibition of forebrain networks caused by the withdrawal of the modulatory influences of norepinephrine (NE) and serotonin (5HT) in REM sleep, secondary to cessation of firing of locus coeruleus and dorsal raphe neurons. This aminergic demodulation can be mathematically modeled as a shift toward increased error at the outputs from neural networks, and these errors might be represented cognitively as incongruities and/or discontinuities. We also consider the possibility that discontinuities are the cognitive concomitant of sudden bifurcations or “jumps” in the responses of forebrain neuronal networks. These bifurcations are caused by phasic discharge of pontogeniculooccipital (PGO) neurons during REM sleep, providing a source of cholinergic modulation to the forebrain which could evoke unpredictable network responses. When phasic PGO activity stops, the resultant activity in the brain may be wholly unrelated to patterns of activity dominant before such phasic stimulation began. Mathematically such sudden shifts from one pattern of activity to a second, unrelated one is called a bifurcation. We propose that the neuronal bifurcations brought about by PGO activity might be represented cognitively as bizarre discontinuities of dream plot. We regard these proposals as preliminary attempts to model the relationship between dream cognition and REM sleep neurophysiology. This neurophysiological model of dream bizarreness may also prove useful in understanding the contributions of REM sleep to the developmental and experiential plasticity of the cerebral cortex.

scholarly journals Bastards, brothers, and unjust warriors: Enmity and ethics in Just War Cinema

2016 ◽  
Vol 43 (1) ◽  
pp. 73-94 ◽  
Author(s):  
Christopher J. Finlay
Keyword(s):  
Cold War ◽  
Just War ◽  
Just War Theory ◽  
Second World War ◽  
Public Understanding ◽  
World War ◽  
Close Attention ◽  
Popular Cinema ◽  
The Relationship ◽  
Second World

AbstractHow do members of the general public come to regard some uses of violence as legitimate and others as illegitimate? And how do they learn to use widely recognised normative principles in doing so such as those encapsulated in the laws of war and debated by just war theorists? This article argues that popular cinema is likely to be a major source of influence especially through a subgenre that I call ‘Just War Cinema’. Since the 1950s, many films have addressed the moral drama at the centre of contemporary Just War Theory through the figure of the enemy in the Second World War, offering often explicit and sophisticated treatments of the relationship between thejus ad bellumand thejus in bellothat anticipate or echo the arguments of philosophers. But whereas Cold War-era films may have supported Just War Theory’s ambitions to shape public understanding, a strongly revisionary tendency in Just War Cinema since the late 1990s is just as likely to thwart them. The potential of Just War Cinema to vitiate efforts to shape wider attitudes is a matter that both moral philosophers and those concerned with disseminating the law of war ought to pay close attention to.

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