Optimal Design Study of High-Order FIR Digital Filters Based on Neural-Network Algorithm

2006 ◽  
Author(s):  
Zhe-zhao Zeng ◽  
Ye Chen ◽  
Yao-nan Wang
Keyword(s):  
Neural Network ◽  
Optimal Design ◽  
Digital Filters ◽  
High Order ◽  
Design Study ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Fir Digital Filters

scholarly journals Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
ChuanDong Yu ◽  
Nan Du
Keyword(s):  
Neural Network ◽  
Landscape Ecology ◽  
Wavelet Neural Network ◽  
High Order ◽  
Average Error ◽  
Ecological Planning ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Landscape Ecological Planning ◽  
Landscape Ecological

Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

scholarly journals Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Zhang ◽  
Qixiang Zhong ◽  
Zhenglun Yu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
High Order ◽  
Diagnosis And Treatment ◽  
Disease Monitoring ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Medical Big Data

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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