Using fuzzy genetic, Artificial Bee Colony (ABC) and simple genetic algorithm for the stiffness optimization of steel frames with semi-rigid connections

Yassami, Mohammad; Ashtari, Payam

doi:10.1007/s12205-014-0517-z

Cited by 23 publications

(7 citation statements)

References 6 publications

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“…In this model, the input data is the first day residuals and the expectation output data is the second day residuals. The network process is mainly divided into two sections, the first one is the forward spread from the input layer through the hidden layer to the output layer; and the other is the error's back propagation, which is contrary to the forward transmission steps [45][46][47]. Adjusting the weights and offsets by the back propagation errors [26,48], can be expressed by:…”

Section: Nonlinear Error Corrections By Using a Bp Neural Network Modelmentioning

confidence: 99%

“…Thus, it is very effective for the BDS satellite clocks to achieve better fitting and forecasting accuracy. When the network weights and biases reach the allowable range, the BPNN training is completed [47][48][49][50][51][52]. However, the computation of the network is more complicated and its convergence rate will slow down.…”

Section: Nonlinear Error Corrections By Using a Bp Neural Network Modelmentioning

confidence: 99%

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An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets

et al. 2018

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Abstract:The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS clock offset series, the GPS ultra-rapid clock model, which uses a simple quadratic polynomial plus one periodic is not suitable for BDS. Therefore, an improved prediction model for BDS satellite clocks is proposed in order to enhance the precision of ultra-rapid predicted clock offsets. First, a basic quadratic polynomial model which is fit for the rubidium (Rb) clock is constructed for BDS. Second, the main cyclic terms are detected and identified by the Fast Fourier Transform (FFT) method according to every satellite clock offset series. The detected results show that most BDS clocks have special cyclic terms which are different from the orbit periods. Therefore, two main cyclic terms are added to absorb the periodic effects. Third, after the quadratic polynomial plus two periodic fitting, some evident nonlinear system errors also exist in the model residual, and the Back Propagation (BP) neural network model is chosen to compensate for these nonlinear system errors. The simulation results show that the performance and precision using the improved model are better than that of China iGMAS ultra-rapid prediction (ISU-P) products and the Deutsches GeoForschungsZentrum GFZ BDS ultra-rapid prediction (GBU-P) products. Comparing to ISU-P products, the average improvements using the proposed model in 3 h, 6 h, 12 h and 24 h are 23.1%, 21.3%, 20.2%, and 19.8%, respectively. Meanwhile the accuracy improvements of the proposed model are 9.9%, 13.9%, 17.3%, and 21.2% compared to GBU-P products. In addition, the kinematic Precise Point Positioning (PPP) example using 8 Multi-GNSS Experiment MGEX stations shows that the precision based on the proposed clock model has improved about 16%, 14%, and 38% in the North (N), East (E) and Height (H) components.

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Section: Nonlinear Error Corrections By Using a Bp Neural Network Modelmentioning

confidence: 99%

Section: Nonlinear Error Corrections By Using a Bp Neural Network Modelmentioning

confidence: 99%

An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets

et al. 2018

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“…Genetic algorithms search for a maximum value using the principle of natural selection and natural genetics [11]. Genetic Algorithms begin by determining the values of genes where a collection of genes will form chromosomes, the set of chromosomes will form individuals, and the set of individuals form the initial population.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimized Fuzzy Backpropagation Neural Network using Genetic Algorithm for Predicting Indonesian Stock Exchange Composite Index

Rifai¹

2019

EECSI

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Investment activities in the capital market have the possibility to generate profits and at the same time also cause losses. The composite stock price index as an indicator used to determine investment continues to change over time. Uncertainty of stock exchange composite index requires investors to be able to make predictions so as to produce maximum profits. The aim of this study is to forecast the composite stock price index. The input variables used are Indonesia interest rates, rupiah exchange rates, Dow Jones index, and world gold prices. All data obtained in the period from January 2008 to March 2019. Data are used to build the Fuzzy Backpropagation Neural Network (FBPNN), model. The weight of FBPNN model was optimized using Genetic Algorithm then used to forecast the composite stock price index. The forecasting result of the composite stock price index for April to June 2019 respectively were 5822.6, 5826.8, and 5767.3 with the MAPE value of 8.42%. These results indicate that Indonesia interest rates, rupiah exchange rate, Dow Jones index, and the gold price are the proper indicators to predict the composite stock price index.

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“…[14][15][16][17][18][19][20][21][22][23] and in many other works. Algorithms for optimal design of constructions combining GAs procedures with other methods of structure optimization were also considered [24][25][26][27]. A feature of many load-bearing structure optimization problems lies in the great number of conditions that significantly narrow the admissible search areas of parameters.…”

Section: Introductionmentioning

confidence: 99%