Artificial Neural Network and Fuzzy Neural Network Algorithm for Financial Health Analysis of Indonesian SOEs

The determining of financial soundness of SOEs company is regulated by the government through Decree of the Minister of SOEs KEP: 100 / BUMN / 2002. There are 8 parameters to be calculated for determining financial soundness such as ROE, ROI, cash ratio, current ratio, collection periods, inventory turnover, TATO, and ratio of total equity to total assets. From the calculation results based on these rules, there are 3 categories of companies, that is healthy, less healthy and unhealthy. To calculate the best parameters as a significant aspect to determining financial soundness, this research using neural networks method. In this paper, it compares the value of accuracy and learning rate with Artificial Neural Network and Fuzzy Neural Network method. Accuracy used as the fitness value of the Genetic Algorithm, to get the top three parameters from eight parameters to determining the financial soundness of SOEs companies. The result of this research both ANN and FNN get the same top three parameters: ROE, ROI, and Cash Ratio. In overall, artificial neural network or fuzzy neural network algorithm both suitable for use in the financial health analysis of SOEs companies.

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Application of the Fuzzy Neural Network Algorithm in the Exploration of the Agricultural Products E-commerce Path

Intelligent Automation & Soft Computing ◽

10.31209/2020.100000174 ◽

2020 ◽

pp. -1--1

Author(s):

Shuangying Liu ◽

Weidong Zhang

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Agricultural Products ◽

Network Algorithm ◽

Fuzzy Neural ◽

Neural Network Algorithm

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A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction

Neural Computing and Applications ◽

10.1007/s00521-021-05962-3 ◽

2021 ◽

Author(s):

Chenxi Huang ◽

Junsheng Zhou ◽

Jinling Chen ◽

Jane Yang ◽

Kathy Clawson ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Performance Prediction ◽

Support Vector ◽

Course Performance ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Algorithm

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Freeway Slope Stability Evaluation Based on Multi-Level Fuzzy Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.430-432.1700 ◽

2012 ◽

Vol 430-432 ◽

pp. 1700-1703

Author(s):

Yan Kai Wu ◽

Xian Song Sang ◽

Bin Niu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Slope Stability ◽

Basic Principle ◽

Fuzzy Neural Network ◽

Stability Assessment ◽

High Slope ◽

Fuzzy Neural ◽

Multi Level ◽

Artificial Neural

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.

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