An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

Mahmud, Mohammad Sultan; Meesad, Phayung

doi:10.1007/s00500-015-1752-z

Cited by 23 publications

(20 citation statements)

References 25 publications

(22 reference statements)

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“…e ACO (ant colony optimization) algorithm simulates the algorithm where ants start from the ant nest and distribute pheromone all the way to find food. Ants use their perception of pheromone concentration to find the shortest path to food [8]. When encountering obstacles, the ants quickly find a new path to the food through mutual cooperation.…”

Section: Research Methods Of Feature Selection Algorithmmentioning

confidence: 99%

[Retracted] Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity

Chen

2021

Complexity

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The swarm intelligence algorithm simulates the behavior of animal populations in nature and is a new type of intelligent solution that is different from traditional artificial intelligence. Feature selection is a very common data dimensionality reduction method, which requires us to select the feature subset with the best evaluation criteria from the original feature set. Feature selection, as an effective data processing method, has become a hot research topic in the fields of machine learning, pattern recognition, and data mining and has received extensive attention and attention. In order to verify the improvement effect of the feature selection algorithm based on the swarm intelligence algorithm on the data, this paper conducts experiments on six classes in the city’s first middle school with similar conditions. First, count the current situation of the students in the class, then divide them into classes, use different algorithms to teach them, and count the changes of the students after a period of teaching. The experiment found that the performance of students under the feature selection algorithm is about 30% higher than other teaching methods, and the awareness of cooperation between students reaches 0.8. It solves the contradiction between popularization and improvement and solves the problems of polarization and transformation of underachievers. The individuality of the algorithm has been fully utilized and developed. The test results show that the improved algorithm has faster convergence speed and higher solution accuracy, and the feature selection algorithm based on swarm intelligence algorithm can effectively improve the efficiency of the algorithm.

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Section: Research Methods Of Feature Selection Algorithmmentioning

confidence: 99%

[Retracted] Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity

Chen

2021

Complexity

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“…However, neural networks tend to fall into a local minimum in the time series prediction problem. In response to this defect, BPNN and Adaptive Differential Evolution Algorithm (ADE) are combined to present a hybrid model [15][16][17][18][19], namely ADE-BPNN to improve the goodness of fit of sample data for time series analysis. And using two real data sets, the operability and good performance of the proposed hybrid method are confirmed, and the proposed ADE-BPNN method can significantly increase the good fitting performance compared with separate models such as BPNN or ARIMA.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence algorithm for optimal time series data model

Wang

2024

IEEE Access

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In order to solve the limitation of a large number of literatures on the study of modeling, simulation and prediction of time series data, there is no model selection, and a certain model is directly used for analysis. For three types of artificial intelligence models often applied to time series analysis: hidden Markov Carrier model, artificial neural network model and autoregressive moving average model are used to study model selection based on simulation comparison method. The study of nonlinear integration methods, using intelligent system methods to learn the weighting mode, has made the model's generalization ability and the degree is of fit to the sample data have been significantly improved. At the same time, numerical simulations are performed on various models, and the characteristics of the time series generated by various models are investigated. Based on the characteristics, the theory and algorithm of model selection are proposed. The model selection theory and algorithm in this paper is used for empirical analysis. For the artificial intelligence models commonly used in time series analysis such as autoregressive moving average model, artificial neural network model, hidden Markov model, etc., when selecting the research model, the method of simulation comparison can be used. The experimental results show that the time series data generated by various models have different mathematical and physical characteristics, which provide a basis for model selection. At the same time, the selection theory is practical. The model selected by the theory has a good fit and prediction effect. The generation of different models has different mathematical characteristics of time series data, which also provides a basis for selecting models.

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“…In time-series prediction applications, neural networks (NNs) usually employed prediction error as an additional input of the networks. This has been proven to yield superior performance compared with that without error feedback (Connor et al, 1994;Mahmud and Meesad, 2016;Waheeb et al, 2016). The error feedback determined the difference between the network output and the target value.…”

Section: Introductionmentioning

confidence: 99%

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Yang

Wang

et al. 2020

Front. Comput. Neurosci.

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An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

Cited by 23 publications

References 25 publications

[Retracted] Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity

[Retracted] Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity

Artificial intelligence algorithm for optimal time series data model

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

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