scholarly journals Performance prediction of construction projects using soft computing methods

2019 ◽  
Vol 46 (7) ◽  
pp. 609-620 ◽  
Author(s):  
Seyedeh Sara Fanaei ◽  
Osama Moselhi ◽  
Sabah T. Alkass
Keyword(s):  
Performance Prediction ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Performance Indicator ◽  
Relative Weight ◽  
Computing Methods ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Project Data

Key performance indicators (KPIs) evaluate different aspects of projects and are used to determine the health status of projects. While there is considerable work on project quantitative performance prediction, less attention, however, has been directed towards qualitative performance prediction. This paper offers a novel framework for qualitatively measuring and predicting six important construction project KPIs using the neuro-fuzzy technique. Neuro-fuzzy models are developed to map the KPIs of three critical project stages to whole project KPIs. Subtractive clustering is utilized to automatically generate initial fuzzy inference system (FIS) models and the artificial neural network (ANN) technique is used to tune the parameters of the initial FIS models. The relative weight of each KPI is determined using a series of computing methods namely, analytic hierarchy process (AHP) and genetic algorithm (GA), to generate the performance indicator (PI). The developed models are validated with real project data showing that the rate of error is reasonably low. The results show that the AHP method is more accurate when compared to the GA method. This framework can be used in building construction projects to help decision-makers evaluate the performance of their projects.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 5 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen I. Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

Integrated real-time flood forecasting and inundation analysis in small-medium streams

2019 ◽  
Author(s):  
Byunghyun Kim ◽  
Seung-Yong Choi ◽  
Kun-Yeun Han
Keyword(s):  
Real Time ◽  
Fuzzy Inference ◽  
Flood Forecasting ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Lead Times ◽  
Hydrodynamic Models ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small-medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed data without abundant physical, and can be performed in real time by integrating point- and section flood forecasting and spatial inundation analysis.

Thermal Performance Prediction of Two-Phase Closed Thermosyphon Using Adaptive Neuro-Fuzzy Inference System

2014 ◽  
Vol 36 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Mehdi Shanbedi ◽  
Ahmad Amiri ◽  
Sajjad Rashidi ◽  
Saeed Zeinali Heris ◽  
Majid Baniadam
Keyword(s):  
Thermal Performance ◽  
Performance Prediction ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Two Phase ◽  
Inference System ◽  
Neuro Fuzzy

An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

2014 ◽  
Vol 501-504 ◽  
pp. 391-394
Author(s):  
Yi Ming Xiang ◽  
Xue Yan Liu ◽  
Gui Xiang Ling ◽  
Bin Du
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Subtractive Clustering ◽  
Forecast Errors ◽  
Frost Heaving ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

scholarly journals Spike discharge prediction based on Neuro-fuzzy system

2017 ◽  
Author(s):  
Mahdi Zarei
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Subtractive Clustering ◽  
Inference System ◽  
Spike Discharge ◽  
Neuro Fuzzy ◽  
Spike Latency ◽  
Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

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