scholarly journals Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

2009 ◽  
Vol 2009 ◽  
pp. 1-22 ◽  
Author(s):  
C. D. Tilakaratne ◽  
M. A. Mammadov ◽  
S. A. Morris
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Global Optimization ◽  
Stock Market ◽  
Historical Data ◽  
Error Function ◽  
Feedforward Neural Networks ◽  
Ordinary Least Squares ◽  
Global Optimization Algorithm ◽  
Network Algorithms

The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is imbalanced. The modified network algorithms are based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLSs) error function. An adjustment relating to the contribution from the historical data used for training the networks and penalisation of incorrectly classified trading signals were accounted for, when modifying the OLS function. A global optimization algorithm was employed to train these networks. These algorithms were employed to predict the trading signals of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.

FEEDFORWARD NEURAL NETWORK MODELS FOR HANDLING CLASS OVERLAP AND CLASS IMBALANCE

2005 ◽  
Vol 15 (05) ◽  
pp. 323-338 ◽  
Author(s):  
RALF KRETZSCHMAR ◽  
NICOLAOS B. KARAYIANNIS ◽  
FRITZ EGGIMANN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Function ◽  
Class Imbalance ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Real World Data ◽  
Special Case

This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-controlled neural networks (VCNNs), which are developed to handle class overlap by minimizing an error function involving the class-specific variance (CSV) computed at their outputs. Another special case of the proposed error function can be used for training class-balancing neural networks (CBNNs), which are developed to handle class imbalance by relying on class-specific correction (CSC). VCNNs and CBNNs are compared with conventional feedforward neural networks (FFNNs), quantum neural networks (QNNs), and resampling techniques. The properties of VCNNs and CBNNs are illustrated by experiments on artificial data. Various experiments involving real-world data reveal the advantages offered by VCNNs and CBNNs in the presence of class overlap and class imbalance.

scholarly journals Feedforward Neural Networks with a Hidden Layer Regularization Method

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 525 ◽  
Author(s):  
Habtamu Alemu ◽  
Wei Wu ◽  
Junhong Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Structure ◽  
Regularization Method ◽  
Error Function ◽  
Feedforward Neural Networks ◽  
Training Process ◽  
Regularization Term ◽  
Neural Network Structure ◽  
Hidden Layer

In this paper, we propose a group Lasso regularization term as a hidden layer regularization method for feedforward neural networks. Adding a group Lasso regularization term into the standard error function as a hidden layer regularization term is a fruitful approach to eliminate the redundant or unnecessary hidden layer neurons from the feedforward neural network structure. As a comparison, a popular Lasso regularization method is introduced into standard error function of the network. Our novel hidden layer regularization method can force a group of outgoing weights to become smaller during the training process and can eventually be removed after the training process. This means it can simplify the neural network structure and it minimizes the computational cost. Numerical simulations are provided by using K-fold cross-validation method with K = 5 to avoid overtraining and to select the best learning parameters. The numerical results show that our proposed hidden layer regularization method prunes more redundant hidden layer neurons consistently for each benchmark dataset without loss of accuracy. In contrast, the existing Lasso regularization method prunes only the redundant weights of the network, but it cannot prune any redundant hidden layer neurons.

scholarly journals A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes

2017 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
D. Geraldine Bessie Amali ◽  
Dinakaran M.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Global Optimization ◽  
Optimization Algorithms ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Network Training ◽  
Multimodal Function ◽  
Artificial Neural

Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization.

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

scholarly journals Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

2020 ◽  
Vol 5 (9) ◽  
pp. 1124-1130
Author(s):  
Ledisi Giok Kabari ◽  
Young Claudius Mazi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Neural Networks ◽  
Niger Delta ◽  
Feedforward Neural Networks ◽  
Flood Prediction ◽  
The Neural Network ◽  
Significant Damage ◽  
Weather Underground ◽  
Short Term Prediction

Climate change generates so many direct and indirect effects on the environment.  Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta   sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com.  An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta   sub region of Nigeria.

scholarly journals Comparison of the efficiency of neural network algorithms in recognition and classification problems

2020 ◽  
Vol 224 ◽  
pp. 01025
Author(s):  
Alexey Beskopylny ◽  
Alexandr Lyapin ◽  
Nikita Beskopylny ◽  
Elena Kadomtseva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computation Time ◽  
Classification Problems ◽  
Handwritten Digit Recognition ◽  
Network Algorithms ◽  
Digit Recognition ◽  
Labour Intensity ◽  
Handwritten Digit ◽  
Accuracy Of Results

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

Congestion Prediction System With Artificial Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 28-43
Author(s):  
Fatma Gumus ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Prediction Algorithm ◽  
Learning Stage ◽  
Network Algorithms ◽  
Prediction Scheme ◽  
Machine Learning Approach ◽  
Congestion Prediction ◽  
Nonlinear Autoregressive

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

Sign in / Sign up

Export Citation Format

Share Document