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Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization...
Persistent link: https://www.econbiz.de/10012268496
Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network...
Persistent link: https://www.econbiz.de/10012268745
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Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high...
Persistent link: https://www.econbiz.de/10011921960
This study attempts to accelerate the learning ability of an artifcial electric feld algorithm (AEFA) by attributing it with two mechanisms: elitism and oppositionbased learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fne-tuned solutions obtained thus...
Persistent link: https://www.econbiz.de/10014530164
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