A weighted fuzzy process neural network model and its application in mixed-process signal classification

Xu, Shengli; Feng, Naidan; Liu, Kun; Liang, Yongquan; Liu, Xiaoyu

doi:10.1016/j.eswa.2021.114642

Cited by 11 publications

(5 citation statements)

References 30 publications

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“… ω learning method: this is a learning method with known sample results. X is taken as the input vector of the neural network, and the network output vector Y is obtained under the joint action of the connection weights between each neuron [ 19 ]. The essence of the ω learning method is to take the derivative of the quadratic error function, and its concrete implementation is a learning method of gradient derivation.…”

Section: Artificial Intelligence Technologymentioning

confidence: 99%

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

Xie

2022

Journal of Environmental and Public Health

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There are some problems in the creation process of public health visual art works, such as low accuracy and poor content quality. In order to further improve the accuracy and quality of artistic creation, based on artificial intelligence technology, a neural network model and an error backpropagation algorithm are used to analyze artistic creation, so as to obtain the corresponding optimization model. This model can analyze the application of public health visual art creation concepts in the Venice Biennale, so as to obtain the corresponding model calculation results under different indicators. Finally, data comparison is used to verify the accuracy of the model. Relevant studies show that activation values have different trends in connection weights under the action of different factors. The fluctuation of the curve is obvious in the initial state, but when the factor is high, the corresponding connection weight tends to be stable gradually. The range of learning rate in the initial state is relatively small, and the connection weights show a V-shaped change at higher factors. It can be seen from the curve corresponding to the ω learning method that the increase in learning speed will lead to further compression of calculation time, and the curve shows a trend of fluctuation. The corresponding learning error decreases gradually with the increase in factors, which indicates that higher factors will promote the accuracy of connection weights. Artificial intelligence-based visual models of art can calculate public health. It can be seen from the calculation results that the curve corresponding to artistic connotation has the largest range of variation and the highest influence on the model. The theme, form, and style of art all show a linear trend of improvement with the increase in time, while the content of art shows a downward trend. This research can provide support for the application of artificial intelligence theory in the field of public health.

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Section: Artificial Intelligence Technologymentioning

confidence: 99%

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

Xie

2022

Journal of Environmental and Public Health

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“…The results of our research are in line with and strengthen the results of research that has been conducted by several researchers. These researchers include Xu et al [28] who showed that the combination of fuzzy inference with an artificial neural network model was able to increase the accuracy of the model in terms of classifying four types of objects resulting from the discrimination of reservoir waterflooded states based on physical quantity values such as resistivity, acoustic level, and radioactivity level in the oil layer. Likewise, research conducted by the study [29] shows that the fuzzy wavelet neural network (FWNN) method with a combination of PSO techniques and gradient descent optimization provides more efficient model performance results and has higher precision for short-term wind power forecasting.…”

Section: The Learning Process Of the Fw-dnn Modelmentioning

confidence: 99%

Fuzzy Wavelet Dynamic Neural Network Model for Modeling the Number of Tourist Visits to West Nusatenggara Province

Bahri,

Awalushaumi,

Robbaniyyah

2024

IJCMEM

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This research aims to model tourist visit data to West Nusa Tenggara Province (NTB) using a hybrid model, combining the dynamic neural network method as the core model with the wavelet method and fuzzy inference as tools to optimize the model. The model developed in this research is referred to as the Fuzzy Wavelet Dynamic Neural Networks (FW-DNN) Model. The FW-DNN model is a feed-forward dynamic neural network model that utilizes the Wavelet B-spline function as its activation function and TSK (Takagi-Sugeno-Kang) fuzzy inference as the method for information aggregation. The modeling results on both in-sample and out-sample data show that the proposed FW-DNN model is capable of representing the patterns in tourist visit data to NTB quite effectively. Similar results were also observed in the patterns of data for both domestic and international tourist visit numbers. Based on the root of mean square error (RMSE) indicator, the performance of the developed FW-DNN model for aggregated tourist visit data is 95185.09 for in-sample data and 22615.54 for out-sample data. Partial performance analysis of the FW-DNN model for international tourist visit data shows a value of 39848.94 for in-sample data and 5223.86 for out-sample data. Similarly, the FW-DNN model's performance for domestic tourist visit data is 39848.94 for in-sample data and 5223.86 for out-sample data. Practically, the results of this research can be input for the NTB Provincial government in determining tourism management and/or development policies, especially related to the provision of supporting facilities and infrastructure, or the private sector in an effort to optimize the carrying capacity and/or services for tourists visiting NTB.

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“…The adaptive adjustment curves of crossover probability and mutation probability are shown in Figures 4 and 5. [14,15], and this algorithm is simply analyzed here. BP algorithm is an effective algorithm, but it also has some defects in practical application:…”

Section: Improvement Of Adaptive Genetic Algorithmmentioning

confidence: 99%

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

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A weighted fuzzy process neural network model and its application in mixed-process signal classification

Cited by 11 publications

References 30 publications

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

Fuzzy Wavelet Dynamic Neural Network Model for Modeling the Number of Tourist Visits to West Nusatenggara Province

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

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