Symbolic Function Network

2013 ◽  
pp. 293-324
Author(s):  
George S. Eskander ◽  
Amir Atiya
Keyword(s):  
Neural Networks ◽  
Theoretical Basis ◽  
Main Idea ◽  
Model Optimization ◽  
Symbolic Function ◽  
Functional Basis ◽  
Starting Point ◽  
Functional Forms ◽  
Function Network

This chapter reviews a recent HONN-like model called Symbolic Function Network (SFN). This model is designed with the goal to impart more flexibility than both traditional and HONNs neural networks. The main idea behind this scheme is the fact that different functional forms suit different applications and that no specific architecture is best for all. Accordingly, the model is designed as an evolving network that can discover the best functional basis, adapt its parameters, and select its structure simultaneously. Despite the high modeling capability of SFN, it is considered as a starting point for developing more powerful models. This chapter aims to open a door for researchers to propose new formulations and techniques that impart more flexibility and result in sparser and more accurate models. Through this chapter, the theoretical basis of SFN is discussed. The model optimization computations are deeply illustrated to enable researchers to easily implement and test the model.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Abstract P437: Deep Learning-based Models For Complete Atrioventricular Block Heart Rhythm Analysis

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Dahim Choi ◽  
Nam Kyun Kim ◽  
Young H Son ◽  
Yuming Gao ◽  
Christina Sheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Atrioventricular Block ◽  
Prediction Models ◽  
Pacemaker Implantation ◽  
Heart Rhythm ◽  
High Sensitivity ◽  
Ventricular Myocardium ◽  
True Positive Rate ◽  
Starting Point

Atrioventricular block (AVB), caused by impairment in the heart conduction system, presents extreme diversity and is associated with other complications. Only half of AVB patients require a permanent pacemaker, and the process determining the pacemaker implantation is associated with an increase in cost and patient morbidity and mortality. Thus, there is a need for models capable of accurately identifying transient or reversible causes for conduction disturbances and predicting the patient risks and the necessity of a pacemaker. Deep learning (DL) is brought to the forefront due to its prediction accuracy, and the DL-based electrocardiogram (ECG) analysis can be a breakthrough to analyze a massive amount of data. However, the current DL models are unsuitable for AVB-ECG, where the P waves are decoupled from the QRS/T waves, and a black-box nature of the DL-based model lowers the credibility of prediction models to physicians. Here, we present a real-time-capable DL-based algorithm that can identify AVB-ECG waves and automate AVB phenotyping for arrhythmogenic risk assessment. Our algorithm can analyze unformatted ECG records with abnormal patterns by integrating the two representative DL algorithms: convolutional neural networks (CNN) and recurrent neural networks (RNN). This hybrid CNN/RNN network can memorize local patterns, spatial hierarchies, and long-range temporal dependencies of ECG signals. Furthermore, by integrating parameters derived from dimension reduction analysis and heart rate variability into the hybrid layers, the algorithm can capture the P/QRS/T-specific morphological and temporal features in ECG waveforms. We evaluated the algorithm using the six AVB porcine models, where TBX18, a pacemaker transcription factor, was transduced into the ventricular myocardium to form a biological pacemaker, and an additional electronic pacemaker was transplanted as a backup pacemaker. We achieved high sensitivity (95% true positive rate) and quantified the potential risks of various pathological ECG patterns. This study may be a starting point in conducting both retrospective and prospective patient studies and will help physicians understand its decision-making workflow and find the incorrect recommendations for AVB patients.

Autoencoder for ecg signal outlier processing in system of biometric authentication

2019 ◽  
Vol 24 (1-2) ◽  
pp. 108-117
Author(s):  
Khoma V.V. ◽  
◽  
Khoma Y.V. ◽  
Khoma P.P. ◽  
Sabodashko D.V. ◽  
...  
Keyword(s):  
Neural Networks ◽  
Statistical Approach ◽  
Main Idea ◽  
Biometric Identification ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
Lower Accuracy ◽  
Novel Method ◽  
Waveform Distortions ◽  
The One

A novel method for ECG signal outlier processing based on autoencoder neural networks is presented in the article. Typically, heartbeats with serious waveform distortions are treated as outliers and are skipped from the authentication pipeline. The main idea of the paper is to correct these waveform distortions rather them in order to provide the system with better statistical base. During the experiments, the optimum autoencoder architecture was selected. An open Physionet ECGID database was used to verify the proposed method. The results of the studies were compared with previous studies that considered the correction of anomalies based on a statistical approach. On the one hand, the autoencoder shows slightly lower accuracy than the statistical method, but it greatly simplifies the construction of biometric identification systems, since it does not require precise tuning of hyperparameters.

scholarly journals Anthropological Foundations of Global Education

2020 ◽  
Vol 26 (1) ◽  
pp. 270-281
Author(s):  
Andrzej Gzegorczyk
Keyword(s):  
Student Development ◽  
Cultural Production ◽  
Educational System ◽  
Global Education ◽  
Human Life ◽  
Main Idea ◽  
Modern World ◽  
Human Environment ◽  
Starting Point ◽  
The Individual

The first Ukrainian translation of the text by Andrzej Grzegorczyk "Anthropological Foundations of Global Education". Andrzej Grzegorczyk (1922-2014) asks the question: is the current construction of the world educational system theoretically justified in terms of human cognitive needs in the modern world, and does it have prospects for development? The theoretical starting point for the rational substantiation of the construction of a modern educational program needed in our era can be represented by distinguishing two components of the picture of human life: 1) stages of development of knowledge of the child and 2) branches (spheres) of human activity to which the school should prepare. Andrzej Grzegorczyk offers his own vision of the sequence of formation of the student's personality. Based on the achievements of socio-evolutionary psychology, he proposes to correlate ontogenesis and phylogeny in education. The young human individual goes, in particular, through successive phases of development, in each of which in turn is dominated by the following four educational and developmental processes initiated by the natural human environment (as well as school). The stages of learning correspond, thus, to the prospects of student development: from the narrowest (family-tribal) perspective to the universalist, which is a synthesis of what tradition brings, as well as acquired knowledge and development of a sense of universal values. Thus, the stages (levels) of education can, in his opinion, be called as follows: 1) family-tribal, 2) traditionally national-religious, 3) individual-rationalist-scientific, 4) universalist-synthetic. The second dimension of the education program is the field / field of study. Presenting the problems of creative realization of values ​​in public life, they can be arranged according to certain parameters: guidelines for activity, way of seeing one's place in society, forms (mechanisms) of action to which the individual is usually subject or implements at this stage, related norms and positions. Among the positions of special attention deserves the experience of self-worth. In the formation of the educational system should include in the content of education the following topics related to culture, the following parameters: type of culture, the main idea of ​​culture of this type, characteristics of the richness of cultural production of this type and related type of knowledge.

Sustainable urban land use: Definition, theoretical basis and review of morphology aspect concepts

2009 ◽  
Vol 1 (2) ◽  
pp. 153-170
Author(s):  
Ranka Gajić
Keyword(s):  
Land Use ◽  
Theoretical Basis ◽  
Urban Land ◽  
Urban Land Use ◽  
Point Of View ◽  
Theory And Practice ◽  
Urban Planner ◽  
Starting Point ◽  
Definition Of ◽  
Spatial Entities

The topic of sustainable urban land use compared to the world theory and practice has almost not been elaborated by the professionals in Serbia. This paper's starting point is that it is important to analyze and apply this topic, not only for the master plan level but also for the more detailed levels of planning and for smaller spatial entities/complexes in the cities, focusing on the morphological implications of sustainable urban land use as the topic relevant from the architect/urban planner point of view. After the definition of the notion of sustainable urban land use and the theoretical basis has been defined in the introductory explications, followed by a brief review of that topic's presence in Serbia, the point of view has been explained - namely, focusing on one single aspect (morphology) followed by a review of relevant criteria of other aspects of sustainable urban land use (economical, ecological and social aspects). The conclusion derived by synthesis represents the recommendation for a possible practice/methodology for planner's approach to the sustainable urban land use from the viewpoint of the morphology aspect.

Artificial Neural Networks and Their Applications in Business

2018 ◽  
pp. 6642-6657
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

Artificial Neural Networks and Their Applications in Business

2019 ◽  
pp. 1009-1027
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

Systems Theory and Introducing Action

2020 ◽  
pp. 91-112
Author(s):  
Catherine Raeff
Keyword(s):  
Systems Theory ◽  
Theoretical Basis ◽  
Current Work ◽  
Starting Point ◽  
Multiple Processes ◽  
Complex Phenomena

The goals of this chapter are to summarize systems theory, which provides an overarching theoretical basis for the current work, and to introduce action as the key concept that will be conceptualized in more detail in subsequent chapters. Systems theory is the starting point for the current work because it is based on integrative and relational assumptions and because it offers a way of understanding complex phenomena in terms of multiple processes that mutually affect each other. In this chapter, systems theory is further summarized in terms of connections among parts and wholes, multiple kinds of causality, emergence, stability, and variability. Action is then identified as the wider whole or system that represents what people do. The chapter ends by acknowledging some of the values that inform how the author is thinking about action.

scholarly journals Fast battery capacity estimation using convolutional neural networks

2020 ◽  
pp. 014233122096642 ◽  
Author(s):  
Yihuan Li ◽  
Kang Li ◽  
Xuan Liu ◽  
Li Zhang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Smart Grids ◽  
Estimation Method ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Capacity Estimation ◽  
Starting Point ◽  
Battery Capacity ◽  
Mean Square Errors

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

Simulation and Analysis about Different Pressure Angle in Involute Gears Based on Neural Network

2014 ◽  
Vol 540 ◽  
pp. 88-91 ◽  
Author(s):  
Jun Xiao ◽  
Xu Lei Deng ◽  
Jia Ning He ◽  
Wu Xing Ma ◽  
Yan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Theoretical Basis ◽  
Nonlinear Mapping ◽  
Pressure Angle ◽  
Involute Gear ◽  
Transmission Design ◽  
Involute Gears ◽  
The Neural Networks

This article introduced neural network, discusses the neural networks model and its learning process. Using the MATLAB environment research and analysis the involute gear undercutting relationship, which under different pressure angles. In the number of teeth or modulus has been scheduled environment apply the nonlinear mapping characteristics of neural networks to involute gear undercutting do a more accurate simulation. This provides a theoretical basis for different pressure angle involute gear in gear transmission design.

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