Prediction method of construction land expansion speed of ecological city based on BP neural network

Li, Anlin; Niu, Lede; Zhou, Yan

doi:10.1504/ijetm.2022.120722

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…To address the bad prediction accuracy and poor convergence performance of the conventional urban construction land model, Li et al proposed a prediction model for the speed of sustainable city construction land based on BP. Experiments proved that the proposed model had good convergence and high accuracy [8]. Jinlong et al raised an evaluation model based on BP to forecast the ultra-high cycle fatigue life of centrifugal impellers.…”

Section: Related Wordsmentioning

confidence: 98%

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The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

Zhang

2023

Int. J. Simul. Multidisci. Des. Optim.

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In the context of e-commerce trade between enterprises, there are problems such as price wars, false advertising, and unreasonable operations aimed at seizing market share. However, traditional estimation methods cannot provide a reasonable evaluation of the marketing competitiveness of e-commerce enterprises from an overall perspective. To help enterprises better clarify their own position and quantify their marketing competitiveness, a competitiveness evaluation model based on optimized deep learning networks is proposed. By combining subjective and objective evaluation methods, the indicators that affect the marketing competitiveness of enterprises are assigned to obtain the final competitiveness value. The research outcomes expressed that the max absolute error of the model constructed by the research institute was 0.0006, the max relative error was 0.0045, and the model accuracy was 99.85%. In the secondary indicator experiment of marketing competition, the model determined that the turnover rate of fixed assets had the greatest impact, with a weight value of 0.1263. Nine companies were randomly selected for market value estimation, and the average relative error of the model was 14.90%, which was lower than the mean relative error of the relative valuation, cash flow and absolute valuation methods, with numerical differences of 8.03%, 2.94%, and 0.12%, respectively. The research findings illustrated that the model constructed by the research institute had good performance and certain reference values for evaluating the marketing competitiveness of e-commerce enterprises.

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Section: Related Wordsmentioning

confidence: 98%

“…In formula (7), w jk (p + 1) denotes the connection weight between neuron j and neuron k at (p + 1)th, and △w jk (p) indicates the weight adjusted during p + 1 iteration. The adjusted weight calculation formula is shown in (8).…”

Section: Model Construction Of Competitiveness Evaluation Indicators ...mentioning

confidence: 99%

The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

Zhang

2023

Int. J. Simul. Multidisci. Des. Optim.

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“…Therefore, the links of dendrites → axons → dendrites → axons constitute a huge human neural network system. BP (back-propagation) neural networks are the first and currently more developed artificial intelligence network technology, which uses error back-propagation to train data [6].…”

Section: Introductionmentioning

confidence: 99%

Safety Risk Evaluation of Tourism Management System Based on PSO-BP Neural Network

Song

Han

2023

Wireless Communications and Mobile Computing

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With the recovery of the tourism industry and the development of the field of artificial intelligence, the application of intelligent neural network technology to management systems for safety risk assessment is the choice of the times and an urgent real need for relevant practitioners. In this study, the BP neural network algorithm is used as a tool for safety evaluation of the tourism management system. The three-layer structure of the BP neural network and the role of nodes in it are introduced, the weight values and thresholds of nodes in each of the three layers are calculated, and the particle swarm algorithm is added to optimize the model. In the practical stage, the tourism data of a place in Switzerland was selected as the training data. After 12,859 iterations, the model achieved the best calibration error of 0.126. After 300 iterations of learning, the BP algorithm optimized with the PSO algorithm has a faster convergence rate, which indicates that the performance of the optimized algorithm has improved significantly and has the global search capability that was not available before, which significantly outperforms the FastText and LSTM models. With the increase in the number of samples of tourism data, macroaccuracy always remains above 80%, so it proves that the optimization algorithm used in this study is an effective and reliable model.

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ESSCC: A new multi-scale and extensively applicable evaluation system for the construction of sponge cities

Ding,

Lu,

Cui

et al. 2023

Sustainable Horizons

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Prediction method of construction land expansion speed of ecological city based on BP neural network

Cited by 3 publications

References 15 publications

The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

Safety Risk Evaluation of Tourism Management System Based on PSO-BP Neural Network

ESSCC: A new multi-scale and extensively applicable evaluation system for the construction of sponge cities

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