Fuzzy Artificial Neural Network Model Research Articles

Compressive Strength Prediction of Green Ultra-High Performance Concrete (GUHPC) Based on Adaptive Network-Based Fuzzy Inference System

Green ultra-high performance concrete (GUHPC) is considered to be a new generation of construction materialsthat adapt to sustainable development and is gradually being used in the fields of bridge reinforcement, housefacades, and paving.To improve the efficiency of green ultra-high performance concrete in the experimental stageand to save the component material, the prediction of the 28-day compressive strength of green ultra-highperformance concrete has become a challenging task. According to the published literature, the compressivestrength of concrete is closely related to the material composition such as cement, fly ash, silica fume, sand, etc. Soin this study, 175 groups of related data of GUHPC were collected to form a database, and an artificial neuralnetwork system combined with IF-THEN fuzzy rules was utilized to establish a model that could better predict the28-day compressive strength of GUHPC. Three evaluation indicators, RMSE, R2, and MAPE, indicate that theprediction of the compressive strength of green ultra-high performance concrete made by the model is completelyreliable. Overall,this study successfully proposes a fuzzy artificial neural network model for predicting the 28-daycompressive strength of GUHPC, which provides a viable prediction tool for GUHPC in the experimental stage.

A GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling

Land subsidence is a complicated hazard that artificial intelligence models can model it without approximation and simplification. In this study, for the first time in land subsidence studies, we used and compared the accuracy and efficiency of hybrid fuzzy-gene expression programming (F-GEP) and fuzzy-artificial neural network (F-ANN) models in estimating land subsidence susceptibility modeling in Varamin aquifer of Iran. For this purpose, after selecting and gathering information from fifteen geo-environmental and hydrogeological effectual factors including specific yield, erosion, aquifer thickness, distance of fault, bedrock level, digital elevation model (DEM), annual rainfall, clay thickness, transmissivity (T), soil type, Debi zonation of pumping wells, slope based on DEM, groundwater drawdown in 20 years, land use, and lithological units event based on literature review in the GIS environment, they were first standardized with GIS fuzzy membership functions, and then GEP model was used to integrate the layers. For this step, using 70% of the data (2919 pixels) for the train and 30% (1251 pixels) for the test. Finally, using several statistical criteria and radar image data, the models were validated. We repeat the model on ANN, and our results showed that F-GEP model (with R2 = 0.99 and RMSE = 0.004) is more accurate than F-ANN model (with R2 = 0.94 and RMSE = 0.056) for land subsidence susceptibility modeling in the study area.

Correlations of knowledge-sharing intention between different enterprise types and leadership styles

This study focuses on the correlation of knowledge-sharing intention between family enterprises' and non-family enterprises' leadership styles by taking emotional labour and knowledge inertia as moderating variables. It selects the largest 500 family enterprises and 1,000 non-family enterprises among service industries in Taiwan as the basis of survey and adopts the analytical methods of statistical data and nonlinear fuzzy artificial neural network model. The empirical results: compared with the leaders of non-family enterprises, the leaders of family enterprises are more suitable for the adoption of transformation leadership style; compared with the leaders of non-family enterprises, the leaders of family enterprises have less intention in respect of promotion of knowledge sharing between different departments and divisions; the emotional labour is a factor in the leaders' intention in respect of promotion of knowledge sharing between different departments and divisions; compared with transformation leadership style, transaction leadership style has less intention in respect of promotion of knowledge sharing between different departments and divisions due to the moderating of knowledge inertia.

Correlations of knowledge-sharing intention between different enterprise types and leadership styles

This study focuses on the correlation of knowledge-sharing intention between family enterprises' and non-family enterprises' leadership styles by taking emotional labour and knowledge inertia as moderating variables. It selects the largest 500 family enterprises and 1,000 non-family enterprises among service industries in Taiwan as the basis of survey and adopts the analytical methods of statistical data and nonlinear fuzzy artificial neural network model. The empirical results: compared with the leaders of non-family enterprises, the leaders of family enterprises are more suitable for the adoption of transformation leadership style; compared with the leaders of non-family enterprises, the leaders of family enterprises have less intention in respect of promotion of knowledge sharing between different departments and divisions; the emotional labour is a factor in the leaders' intention in respect of promotion of knowledge sharing between different departments and divisions; compared with transformation leadership style, transaction leadership style has less intention in respect of promotion of knowledge sharing between different departments and divisions due to the moderating of knowledge inertia.

Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

In today’s world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models. Index Terms: Artificial Neural Networks (ANNs), Time series forecasting, Auto-Regressive Integrated Moving Average (ARIMA), Combined forecast, Exchange Rate.

Construction of a service failure severity and recovery model

This study investigates, whether consumers’ personality traits play a critical role in service recovery rate, recovery manner, and perception with varied service failure severity by surveying consumers of restaurant chain stores near north and south Taiwan Science and Technology Parks. The empirical results indicated that the more severe the perceived service failure, the more active and rapid the recovery measures adopted by consumers in Western cultures in comparison to those measures adopted by consumers in Eastern cultures. The player is expected to take more active and rapid recovery measures than the consumer with a more introverted personality. As a result, player response recovery rate will be quicker than that of the more introverted personality. Similarly, the player’s recovery will vary in psychological perceptions of fairness due to the varying personality characteristics of the consumers. This study integrated a linear multivariate analysis and an empirical nonlinear fuzzy artificial neural network model. The related study is unusual in that it varies from previous studies that have focused primarily on Western enterprises. This study considers small to medium enterprises in Taiwan as its sample and examines forward management implications.