scholarly journals Hybrid Ensemble Construction with Selected Neural Networks

2011 ◽  
Vol 15 (6) ◽  
pp. 652-661 ◽  
Author(s):  
M. A. H. Akhand ◽  
◽  
Pintu Chandra Shill ◽  
Kazuyuki Murase ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Task Performance ◽  
Benchmark Problems ◽  
Neural Network Ensemble ◽  
Improve Performance ◽  
Negative Correlation Learning ◽  
Optimal Network ◽  
Correlation Learning ◽  
The Individual

A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.

Paper Currency Recognition System Using Characteristics Extraction and Negatively Correlated NN Ensemble

2011 ◽  
Vol 403-408 ◽  
pp. 915-919 ◽  
Author(s):  
Minal Gour ◽  
Kunal Gajbhiye ◽  
Bhagyashree Kumbhare ◽  
M.M. Sharma
Keyword(s):  
Negative Correlation ◽  
Banking System ◽  
Recognition System ◽  
Shopping Mall ◽  
Image Histogram ◽  
Currency Exchange ◽  
Negative Correlation Learning ◽  
Paper Currency ◽  
Correlation Learning ◽  
The Individual

An efficient currency recognition system is vital for the automation in many sectors such as vending machine, rail way ticket counter, banking system, shopping mall, currency exchange service etc. The paper currency recognition is significant for a number of reasons. a) They become old early than coins; b) The possibility of joining broken currency is greater than that of coin currency; c) Coin currency is restricted to smaller range. This paper discusses a technique for paper currency recognition. Three characteristics of paper currencies are considered here including size, color and texture. By using image histogram, plenitude of different colors in a paper currency is calculated and compared with the one in the reference paper currency. The Markov chain concept has been considered to model texture of the paper currencies as a random process. The method discussed in this paper can be used for recognizing paper currencies from different countries. This paper also represents a currency recognition system using ensemble neural network (ENN). The individual neural networks in an ENN are skilled via negative correlation learning. The purpose of using negative correlation learning is to skill the individuals in an ensemble on different parts or portion of input patterns. The obtainable currencies in the market consist of new, old and noisy ones. It is sometime difficult for a system to identify these currencies; therefore a system that uses ENN to identify them is discussed. Ensemble network is much helpful for the categorization of different types of currency. It minimizes the chances of misclassification than a single network and ensemble network with independent training.

Designing Neural Network Ensembles by Minimizing Mutual Information

2011 ◽  
pp. 1-21 ◽  
Author(s):  
Yong Liu ◽  
Xin Yao ◽  
Tetsuya Higuchi
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Negative Correlation ◽  
Decision Boundary ◽  
Negative Correlation Learning ◽  
Response Sets ◽  
Correlation Learning ◽  
Neural Network Ensembles ◽  
Decision Boundaries ◽  
Network Ensembles

This chapter describes negative correlation learning for designing neural network ensembles. Negative correlation learning has been firstly analysed in terms of minimising mutual information on a regression task. By minimising the mutual information between variables extracted by two neural networks, they are forced to convey different information about some features of their input. Based on the decision boundaries and correct response sets, negative correlation learning has been further studied on two pattern classification problems. The purpose of examining the decision boundaries and the correct response sets is not only to illustrate the learning behavior of negative correlation learning, but also to cast light on how to design more effective neural network ensembles. The experimental results showed the decision boundary of the trained neural network ensemble by negative correlation learning is almost as good as the optimum decision boundary.

An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market

2020 ◽  
pp. 238-256
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Stock Market ◽  
Single Layer ◽  
Second Order ◽  
Optimal Network ◽  
Efficient Prediction ◽  
Higher Order Neural Networks ◽  
The One

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive single layer second order neural network with genetic algorithm based training (ASONN-GA) applied to forecast daily closing prices of the stock market. For comparative study of performance, two conventional neural based models such as a recurrent neural network (RNN) and a multilayer perceptron (MLP) have been developed. The optimal network parameters for all the three models are tuned by genetic algorithm (GA). The efficiencies of the models have been evaluated by forecasting the one-day-ahead closing prices of real stock markets. From simulation studies, it is revealed that the ASONN-GA model achieve better forecasting accuracy over other two models.

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