Forecast model of perceived demand of museum tourists based on neural network integration

Gao, Yuan

doi:10.1007/s00521-020-05012-4

Cited by 12 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e improved ICM model classifier uses the softmax classifier of the ICM model. As the cost function of the softmax classifier of ICM is a non-strict convex function, it is easy to fall into local optimum [19].…”

Section: Classifier Designmentioning

confidence: 99%

Agricultural Product Sales Prediction of ICM Neural Network Improvement by Sparse Autoencoder

2022

Scientific Programming

View full text Add to dashboard Cite

With the rapid development of agricultural product sales data, the traditional prediction model cannot meet the processing needs. Based on deep learning theory, an improved ICM agricultural product sales prediction model using the softmax classifier is proposed. Introducing the sparse autoencoder in ICM can reduce feature loss. The features also can be extracted. In addition, using the pretreatment mode based on fuzzy membership theory, we can obtain the fuzzy correspondence of considerations and grades of agricultural product sales. At the same time, the precision of prediction for the model is further optimized. It can be seen that the agricultural product sales prediction model based on improved ICM can realize the real-time prediction of agricultural product sales. The maximum classification accuracy of the model can reach 80.98%, which means that it has certain practical application value.

show abstract

Section: Classifier Designmentioning

confidence: 99%

Agricultural Product Sales Prediction of ICM Neural Network Improvement by Sparse Autoencoder

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…In other research as demonstrated by (Zlatanovi ć, Sanja), the authors believe that the regional cultural heritage, the national and local administrative authorities, and the interests of investors will all affect the positioning and planning of regional cultural tourism [10,12]. e authors (Heydari Chianeh R et al) believe that for regions with a long history and cultural heritage, it is advisable to take cultural tourism as the leading industry [21]. Deng D. et al established the regional tourism product framework of small towns on the basis of summarizing the tourism literature of small towns [22].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, M characterizes the supreme evolutionary algebra, and, in fact, on the other hand, M is the contemporary evolutionary algebra. is should be noted that P1 is the wrong way round proportionate to the evolutionary algebra (M), while P2 is the wrong way round proportional to the statistical mean or average fitness value [21]. e initial weight and threshold are determined at random, although they have a momentous influence on the performance of the BP neural network.…”

Section: Basics Of the Bp Neural Network And The Improved Geneticmentioning

confidence: 99%

Location Selection of Digital Cultural Tourism Town Based on Improved Genetic Algorithm and BP Neural Network

LeahWu

Qiao

Jia

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Due to the short development time of cultural and tourism towns in China, local governments and investors lack experience in building cultural and tourism towns and do not pay enough attention to the positioning of towns. Alternatively, this issue results in chaos in domestic cultural and tourism towns and even a large number of empty towns in some provinces. Therefore, how to accurately locate cultural tourism towns is a problem that must be studied in depth at present. This paper uses the regional economic theory to collect the influencing factors of cultural tourism town positioning. Based on the BP neural network and the improved genetic algorithm, a genetic neural network model is constructed to train and predict the samples of cultural tourism towns. Taking a small town in the East as a case, the data were collected and analyzed. Established on the prediction outcomes of the genetic neural network, the best location of a small town was selected according to the actual situation of the region. In terms of accuracy and training time, our experimental evaluation confirmed that the neural network enhanced by genetic algorithms outperforms the conventional BP neural network. Furthermore, we observed that besides the classification capabilities of the BP neural network-based model, the classical BP neural network improved by the genetic algorithm also exhibits great macrosearch capabilities and good global optimization performance.

show abstract

“…In the forward transmission process, the signal is processed layer by layer from the input layer through the hidden layer to the output layer. If the output layer cannot obtain the desired output, the signal is transmitted to back propagation, and the weight and threshold are adjusted according to the prediction error, so that the output of BPNN continues to be close to the expected output [13][14][15]. It is known that BPNN containing a hidden layer has sufficient accuracy to approximate any continuous function [16].…”

Section: Error Correction Algorithmmentioning

confidence: 99%

Random Target Localization for an Upper Limb Prosthesis

Zhang

Fan

Wang

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

show abstract

Forecast model of perceived demand of museum tourists based on neural network integration

Cited by 12 publications

References 12 publications

Agricultural Product Sales Prediction of ICM Neural Network Improvement by Sparse Autoencoder

Agricultural Product Sales Prediction of ICM Neural Network Improvement by Sparse Autoencoder

Location Selection of Digital Cultural Tourism Town Based on Improved Genetic Algorithm and BP Neural Network

Random Target Localization for an Upper Limb Prosthesis

Contact Info

Product

Resources

About