scholarly journals Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650261 ◽  
Author(s):  
Andrey Velichko ◽  
Maksim Belyaev ◽  
Vadim Putrolaynen ◽  
Alexander Pergament ◽  
Valentin Perminov
Keyword(s):  
Neural Networks ◽  
Strong Coupling ◽  
Capacitive Coupling ◽  
Chaotic Oscillations ◽  
Physical Mechanisms ◽  
Coupling Capacitance ◽  
Switching Dynamics ◽  
Proposed Model ◽  
Coupling Mode ◽  
Phase Differences

In the present paper, we report on the switching dynamics of both single and coupled VO2-based oscillators, with resistive and capacitive coupling, and explore the capability of their application in oscillatory neural networks. Based on these results, we further select an adequate SPICE model to describe the modes of operation of coupled oscillator circuits. Physical mechanisms influencing the time of forward and reverse electrical switching, that determine the applicability limits of the proposed model, are identified. For the resistive coupling, it is shown that synchronization takes place at a certain value of the coupling resistance, though it is unstable and a synchronization failure occurs periodically. For the capacitive coupling, two synchronization modes, with weak and strong coupling, are found. The transition between these modes is accompanied by chaotic oscillations. A decrease in the width of the spectrum harmonics in the weak-coupling mode, and its increase in the strong-coupling one, is detected. The dependences of frequencies and phase differences of the coupled oscillatory circuits on the coupling capacitance are found. Examples of operation of coupled VO2 oscillators as a central pattern generator are demonstrated.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

A Method of Environmental Sound Classification Based on Residual Networks and Data Augmentation

2021 ◽  
pp. 2150018
Author(s):  
Jinfang Zeng ◽  
Youming Li ◽  
Yu Zhang ◽  
Da Chen
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Environmental Sound ◽  
Sound Classification ◽  
Learning Techniques ◽  
Proposed Model ◽  
Audio Data

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. To date, a variety of signal processing and machine learning techniques have been applied to ESC task, including matrix factorization, dictionary learning, wavelet filterbanks and deep neural networks. It is observed that features extracted from deeper networks tend to achieve higher performance than those extracted from shallow networks. However, in ESC task, only the deep convolutional neural networks (CNNs) which contain several layers are used and the residual networks are ignored, which lead to degradation in the performance. Meanwhile, a possible explanation for the limited exploration of CNNs and the difficulty to improve on simpler models is the relative scarcity of labeled data for ESC. In this paper, a residual network called EnvResNet for the ESC task is proposed. In addition, we propose to use audio data augmentation to overcome the problem of data scarcity. The experiments will be performed on the ESC-50 database. Combined with data augmentation, the proposed model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches in terms of classification accuracy.

A stochastic reaction-diffusion model for protein aggregation on DNA

2017 ◽  
Vol 28 (08) ◽  
pp. 1750102 ◽  
Author(s):  
Nikolaos K. Voulgarakis
Keyword(s):  
Electrostatic Interactions ◽  
Reaction Diffusion ◽  
Control Parameters ◽  
Physical Mechanisms ◽  
Softening Behavior ◽  
Vital Functions ◽  
Reaction Diffusion Model ◽  
Proposed Model ◽  
Negative Supercoiling ◽  
Stochastic Reaction

Vital functions of DNA, such as transcription and packaging, depend on the proper clustering of proteins on the double strand. The present study investigates how the interplay between DNA allostery and electrostatic interactions affects protein clustering. The statistical analysis of a simple but transparent computational model reveals two major consequences of this interplay. First, depending on the protein and salt concentration, protein filaments exhibit a bimodal DNA stiffening and softening behavior. Second, within a certain domain of the control parameters, electrostatic interactions can cause energetic frustration that forces proteins to assemble in rigid spiral configurations. Such spiral filaments might trigger both positive and negative supercoiling, which can ultimately promote gene compaction and regulate the promoter. It has been experimentally shown that bacterial histone-like proteins assemble in similar spiral patterns and/or exhibit the same bimodal behavior. The proposed model can, thus, provide computational insights into the physical mechanisms used by proteins to control the mechanical properties of the DNA.

scholarly journals BEHAVIORAL FUNCTIONS IMPLEMENTATION ON SPIKING NEURAL NETWORKS

2021 ◽  
Vol 20 (3) ◽  
pp. 591-622
Author(s):  
Anton Korsakov ◽  
Aleksandr Bakhshiev ◽  
Lyubov Astapova ◽  
Lev Stankevich
Keyword(s):  
Neural Networks ◽  
Conditioned Reflex ◽  
Negative Reinforcement ◽  
Input Pulse ◽  
Compartmental Models ◽  
Structural Adaptation ◽  
Spike Model ◽  
Current State ◽  
Proposed Model ◽  
Spiking Model

The question of behavioral functions modeling of animals (in particular, the modeling and implementation of the conditioned reflex) is considered. The analysis of the current state of neural networks with the possibility of structural reconfiguration is carried out. The modeling is carried out by means of neural networks, which are built on the basis of a compartmental spiking model of a neuron with the possibility of structural adaptation to the input pulse pattern. The compartmental spike model of a neuron is able to change its structure (the size of the cell body, the number and length of dendrites, the number of synapses) depending on the incoming pulse pattern at its inputs. A brief description of the compartmental spiking model of a neuron is given, and its main features are noted in terms of the possibility of its structural reconfiguration. The method of structural adaptation of the compartmental spiking model of the neuron to the input pulse pattern is described. To study the work of the proposed model of a neuron in a network, the choice of a conditioned reflex as a special case of the formation of associative connections is justified as an example. The structural scheme and algorithm of formation of a conditioned reflex with both positive and negative reinforcement are described. The article presents a step-by-step description of experiments on the associative connection’s formation in general and conditioned reflex (both with positive and negative reinforcement), in particular. The conclusion is made about the prospects of using spiking compartmental models of neurons to improve the efficiency of the implementation of behavioral functions in neuromorphic control systems. Further promising directions for the development of neuromorphic systems based on spiking compartmental models of the neuron are considered.

scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

Evolving deep neural networks for Time Series Forecasting

2021 ◽  
Vol 18 (2) ◽  
pp. 40-55
Author(s):  
Lídio Mauro Lima Campos ◽  
◽  
Jherson Haryson Almeida Pereira ◽  
Danilo Souza Duarte ◽  
Roberto Célio Limão Oliveira ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Good Prediction ◽  
Biologically Inspired ◽  
Price Prediction ◽  
Error Measures ◽  
Good Ability ◽  
Proposed Model

The aim of this paper is to introduce a biologically inspired approach that can automatically generate Deep Neural networks with good prediction capacity, smaller error and large tolerance to noises. In order to do this, three biological paradigms were used: Genetic Algorithm (GA), Lindenmayer System and Neural Networks (DNNs). The final sections of the paper present some experiments aimed at investigating the possibilities of the method in the forecast the price of energy in the Brazilian market. The proposed model considers a multi-step ahead price prediction (12, 24, and 36 weeks ahead). The results for MLP and LSTM networks show a good ability to predict peaks and satisfactory accuracy according to error measures comparing with other methods.

Time Series Based House Sale Value Market Forecasting Using Genetically Evolved Neural Networks

2010 ◽  
pp. 265-282
Author(s):  
George S. Atsalakis ◽  
Kimon P. Valavanis ◽  
Constantin Zopounidis ◽  
Dimitris Nezis
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Housing Prices ◽  
Individual Investors ◽  
Mean Square ◽  
Ann Model ◽  
Neuro Fuzzy ◽  
Proposed Model

Accurate forecasting of the house sale value market is important for individual investors, business investors, banks and mortgage companies. This chapter uses fundamentals of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to derive and implement a hybrid, genetically evolved feedforward ANN model that predicts next month house sale prices. Derived model results are compared with results obtained using a linear regression model and an Adaptive Neuro Fuzzy Inference System (ANFIS). The proposed model returned lower Root Mean Square Error (RMSE), Absolute Mean Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) results compared with the linear regression and ANFIS models. For case studies real monthly data of USA housing prices from 1963 to 2007 were used.

scholarly journals Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery

2020 ◽  
Author(s):  
Christina Corbane ◽  
Vasileios Syrris ◽  
Filip Sabo ◽  
Panagiotis Politis ◽  
Michele Melchiorri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Human Settlements ◽  
Modeling Methodology ◽  
Proposed Model ◽  
Reference Layer ◽  
Sentinel 2 ◽  
Core Features

Abstract Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.

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