Evolving Fuzzy Neural Networks by Particle Swarm Optimization with Fuzzy Genotype Values

In last decade, neural networks (NNs) have been proposed to predict credit risk because of their advantages of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a “black box” syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which strongly limited their applications in practice. To overcome NN’s drawbacks, this paper presents a hybrid system that merges fuzzy neural network and niche evolution particle swarm optimization into a comprehensive mode, named as niche evolution particle swarm optimization fuzzy neural network (NEPSO-FNN), the new model has been applied to credit risk prediction based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of the proposed model is much better than the one of NN model using the cross-validation approach.

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Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽

10.3390/en14041213 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1213

Author(s):

Ahmed Aljanad ◽

Nadia M. L. Tan ◽

Vassilios G. Agelidis ◽

Hussain Shareef

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Solar Irradiance ◽

Particle Swarm ◽

Time Interval ◽

Time Intervals ◽

Swarm Optimization ◽

Backpropagation Neural Networks ◽

Proposed Model ◽

Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

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