scholarly journals First and second order training algorithms for artificial neural networks to detect the cardiac state

2016 ◽  
Vol 7 (2) ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Second Order ◽  
Training Algorithms ◽  
Artificial Neural

Design and implementation of a self-tuning control using artificial neural networks of second order

2006 ◽  
Author(s):  
Mario Ibarra-manzano ◽  
Dora Almanza-ojeda ◽  
Andres Hernandez-Gutierrez ◽  
Juan Amezquita-sanchez ◽  
Luis Lopez-martinez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Second Order ◽  
Design And Implementation ◽  
Self Tuning ◽  
Artificial Neural

Higher Order Neural Networks for Stock Index Modeling

2009 ◽  
pp. 113-131 ◽  
Author(s):  
Yuehui Chen ◽  
Peng Wu ◽  
Qiang Wu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Higher Order ◽  
Stock Index ◽  
Training Algorithms ◽  
Window Method ◽  
Higher Order Neural Networks ◽  
Artificial Neural ◽  
Stock Index Forecasting

Artificial Neural Networks (ANNs) have become very important in making stock market predictions. Much research on the applications of ANNs has proven their advantages over statistical and other methods. In order to identify the main benefits and limitations of previous methods in ANNs applications, a comparative analysis of selected applications is conducted. It can be concluded from analysis that ANNs and HONNs are most implemented in forecasting stock prices and stock modeling. The aim of this chapter is to study higher order artificial neural networks for stock index modeling problems. New network architectures and their corresponding training algorithms are discussed. These structures demonstrate their processing capabilities over traditional ANNs architectures with a reduction in the number of processing elements. In this chapter, the performance of classical neural networks and higher order neural networks for stock index forecasting is evaluated. We will highlight a novel slide-window method for data forecasting. With each slide of the observed data, the model can adjusts the variable dynamically. Simulation results show the feasibility and effectiveness of the proposed methods.

Visualization of Neuro-Fuzzy Networks Training Algorithms

2016 ◽  
pp. 222-257 ◽  
Author(s):  
Antonia Plerou ◽  
Elena Vlamou ◽  
Basil Papadopoulos
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Adaptive Systems ◽  
Automatic Identification ◽  
Training Algorithms ◽  
Fuzzy Logic Systems ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real world problems through the development of intelligent and adaptive systems. Artificial Neural networks are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based signals for training. Ultimately this process may be employed for automatic identification of patterns introduced in medical applications and more specifically for analysis of content produced by brain imaging processes.

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

2021 ◽  
Vol 63 (6) ◽  
pp. 565-570
Author(s):  
Serkan Balli ◽  
Faruk Sen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Modes ◽  
Experimental Studies ◽  
Composite Plates ◽  
Geometric Parameters ◽  
Sandwich Composite ◽  
Geometrical Parameters ◽  
Training Algorithms ◽  
Artificial Neural

Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images

2020 ◽  
Vol 17 (4) ◽  
pp. 1831-1838
Author(s):  
T. Chithambaram ◽  
K. Perumal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Brain Tumor ◽  
Back Propagation ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Training Algorithms ◽  
Back Propagation Algorithm ◽  
Brain Tumor Segmentation ◽  
Artificial Neural

Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.

Sign in / Sign up

Export Citation Format

Share Document