The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Deep Learning for Financial Engineering

“…Wu et al ( 2022 ) established an option trading system on settlement dates that adopted Kelly criterion, support vector machine (SVM), and random forest to improve the trading performance and monitor the investment risk. Yılmaz et al ( 2021 ) proposed the Pi-sigma artificial neural networks (PS-ANN) which training the model by differential evolution algorithm (DEA). In this research, two different datasets were used to verify the forecasting performance of the proposed DEA-PS-ANN method.…”

Analysis of Tension Instability and Research on Stability Control Strategy in the Process of Rapier Loom Weaving

“…As a powerful tool for optimizing processing parameters, it is widely used in various research fields [17]- [18]. Ylma et al [19] used the DE/rand/1 mutation strategy to train a Pi-sigma artificial neural network through a differential evolution algorithm. The performance of the proposed method is evaluated on two datasets, and was found that the proposed method has very effective performance compared with many artificial neural network models.…”

Improved neural network adaptive control for compound helicopter with uncertain cross-coupling in multimodal maneuver

“…On the other hand, some powerful NN such as cerebellar model articulation controller (CMAC) and ridge polynomial neural network (RPNN) usually have heavy structures, which bring too many hyperparameters to design and make these NN much unreliable in engineering [29]. Different from above NN, PSNN is a kind of high order neural network and has received considerable attention recently [30,31] due to its ability of realizing faster nonlinear approximation by introducing both sum and multiplication neurons [32,33]. Meanwhile, simple structure and few hyperparameters are needed in PSNN to improve convergence efficiency compared with other complex neural networks [34].…”