Abstract
Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.
Highlights
As economic policies become more relevant to people’s lives, economic development has become a topic of discussion, and Gross domestic product (GDP), a measure of a country’s or region’s economic performance and level of development, has gradually become a national focus. e country’s or region’s future development trends are measured and evaluated
It is expected that the total economic volume of Shandong province over the seven years will continue to show an upward trend and grow at a high rate, with average annual growth rates of 3.98 percent, 5.63 percent, 6.39 percent, and 6.97 percent, respectively
GDP growth over the five years is expected to be 7.63 percent on average, reaching 1,134.2 billion by 2025; GDP growth over the five years is essentially the same as the growth rate in 2019–2020, is essentially consistent with the growth rate since 2015, and will remain at a higher level than in the previous 50 years. e average annual growth rate of total annual fixed-asset investment (FAI) over the five years, 2022–2026, is forecasted to be 4.79 percent, reaching $801.95 billion in 2025; the growth rate of a total fixed-asset investment over the five years is slightly higher than the growth rate in 2019–2020, but significantly lower than the growth rate since 2010, and will remain moderate and steady
Summary
As economic policies become more relevant to people’s lives, economic development has become a topic of discussion, and GDP, a measure of a country’s or region’s economic performance and level of development, has gradually become a national focus. e country’s or region’s future development trends are measured and evaluated. Because any local minima is global minima if a function is convex, many models try to use convex optimization methods, such as the well-known support vector machine algorithm, which converts the problem into a dyadic problem using KKT conditions, and the dyadic problem is a convex optimization problem, allowing the global minima to be found Because of their powerful nonlinear fitting ability, RBF neural networks can map a wide range of nonlinear relationships. When the RBFNN-GA neural network has a very large number of hidden layers, it can be used to approximate any M-element continuous function with arbitrary accuracy, and the network can always be trained to find a corresponding set of weights to make the best approximation of the unknown nonlinear mapping relation f(−) between the input and output. According to the literature and various theoretical proofs, the combined model’s prediction effect can usually improve the single model to some extent
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