A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model

Arslan, Sule Nazlı; Yolcu, Özge Cağcağ

doi:10.1007/s00521-022-07138-z

Cited by 11 publications

(3 citation statements)

References 106 publications

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“… layer). In this experiment, 151 input variables were selected in the interval [ 3,3]  or the number of iterations reaches 5000 times. Figure 2 shows the network approximation graph of SPSNNGR, and Figure 3 shows the error function graphs of SPSNN algorithm and SPSNNGR algorithm, it can be seen that the SPSNNGR algorithm has good approximation ability and the error of SPSNNGR algorithm decreases faster, which indicates that our proposed algorithm performs better.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term

Huang,

Dong,

Liu

et al. 2024

FCIS

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In recent years, Sigma-Pi-Sigma neural network (SPSNN) as a special kind of higher-order neural network has attracted wide attention for its fast convergence speed and good approximation ability. However, an inappropriate number of hidden layer neurons may also lead to model underfitting or overfitting, which affects the performance and generalization ability of the model. Therefore, we propose a Sigma-Pi-Sigma neural network with graph regularity by adding a graph regularity term to the network. The results show that the proposed algorithm performs well in terms of training accuracy, testing accuracy and efficiency.

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Section: Simulation Resultsmentioning

confidence: 99%

“…It is a higher order neural network with product unit and stronger nonlinear mapping ability, which has its fast convergence speed and good approximation ability [2]. Due to its strong nonlinear mapping ability, this network has been widely used in many fields such as medical and industrial [3][4].…”

Section: Introductionmentioning

confidence: 99%

A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term

Huang,

Dong,

Liu

et al. 2024

FCIS

View full text Add to dashboard Cite

show abstract

“…If the result of fuzzification at time-𝑖 is 𝐴 𝑖 , which 𝐴 𝑖 does not have FLR at condition 𝐴 𝑖 → ∅, and also the maximum value of the degree of its members is at 𝑢 𝑖 , then the value 𝐹 𝑖 is the middle value of 𝑢 𝑖 , which is denoted by 𝑚 𝑖 [16]- [21].…”

Section: Defuzzificationmentioning

confidence: 99%

Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk

Nur,

Nababan,

Gultom

et al. 2023

sitekin

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Investment in stocks is one type of investment that can get huge profits, but there are also great risks. So it is necessary to analyze in advance before starting an investment in stocks, in order to avoid losses. One way is to forecast the stock price using fuzzy time series Cheng. The data used is weekly period stock price data from PTBA in January 2020 - December 2022, which can be categorized as a form of time series. From this research, the forecasting value for the next period is Rp. 3797. Which results in a MAPE of 4.2%, which means that FTS Cheng method is very good to use in forecasting the share price of PT Bukit Asam Tbk, because it produces a MAPE value <10%, and produces an RMSE of 158 rupiah, which means the average of the difference between actual and forecast values.

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A new deep neural network for forecasting: Deep dendritic artificial neural network

Egrioglu,

Bas

2024

Artif Intell Rev

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Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures. It is available in the literature that the use of multiplicative aggregation functions in shallow artificial neural networks produces successful results for the forecasting problem. A type of high-order shallow artificial neural network that uses multiplicative aggregation functions is the dendritic neuron model artificial neural network, which has successful forecasting performance. In this study, the transformation of the dendritic neuron model turned into a multi-output architecture. A new dendritic cell based on the multi-output dendritic neuron model and a new deep artificial neural network is proposed. The training of this new deep dendritic artificial neural network is carried out with the differential evolution algorithm. The forecasting performance of the deep dendritic artificial neural network is compared with basic classical forecasting methods and some recent shallow and deep artificial neural networks over stock market time series. As a result, it has been observed that deep dendritic artificial neural network produces very successful forecasting results for the forecasting problem.

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A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model

Cited by 11 publications

References 106 publications

A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term

A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term

Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk

A new deep neural network for forecasting: Deep dendritic artificial neural network

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