Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design of Fully and Partially Connected Random Neural Networks for Pattern Completion
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Application Study of BP Neural Network on Stock Market Prediction
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 03
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ensemble of neural networks for stock trading decision making
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications: An International Journal
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This paper proposes a novel partially connected neural evolutionary model (Parcone) architecture to simulate the relationship of stock and technical indicators to predict the stock price index. Different from artificial neural networks, the architecture has corrected three drawbacks: (1) connection between neurons of is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and train weights. The more hidden knowledge stored within the historic time series data are needed in order to improve expressive ability of network. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is not defined by sigmoid function but sin(x). The experimental results show that Parcone can make the progress concerning the stock price index and it's very promising to calculate the predictive percentage by simulation results of proposed evolutionary system.