Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening

2021 ◽  
Author(s):  
Zhennan Liu ◽  
Qiongfang Li ◽  
Jingnan Zhou ◽  
Weiguo Jiao ◽  
Xiaoyu Wang
Keyword(s):  
Anfis Model ◽  
Variable Screening ◽  
Model Based ◽  
Runoff Prediction

A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

2011 ◽  
Vol 11 (1) ◽  
pp. 1388-1395 ◽  
Author(s):  
Jing-Rong Chang ◽  
Liang-Ying Wei ◽  
Ching-Hsue Cheng
Keyword(s):  
Anfis Model ◽  
Model Based

scholarly journals A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering

2014 ◽  
Vol 8 (1) ◽  
pp. 833-838 ◽  
Author(s):  
Feng-Yi Zhang ◽  
Zhi-Gao Liao
Keyword(s):  
Empirical Study ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Prediction Ability ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.

An optimized Software defect prediction model based on PSO - ANFIS

2020 ◽  
Vol 13 ◽  
Author(s):  
Misha Kakkar ◽  
Sarika Jain ◽  
Abhay Bansal ◽  
P.S. Grover
Keyword(s):  
Software Development ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Model Based ◽  
Software Defect ◽  
Novel Approach ◽  
Development Teams ◽  
Software Development Teams

Introduction : The Software defect prediction (SDP) model plays a very important role in today’s software industry. SDP models can provide either only a list of defect-prone classes as output or the number of defects present in each class. This output can then be used by quality assurance teams to effectively allocate limited resources for validating software products by putting more effort into these defect-prone classes.The study proposes an OANFIS-SDP model that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. Method: OANFIS is a novel approach based on the Adaptive neuro-fuzzy inference system (ANFIS), which is optimized using Particle swarm optimization (PSO). OANFIS model combines the flexibility of ANFIS model with the optimization capabilities of PSO for better performance. Results: The proposed model is tested using the dataset from open source java projects of varied sizes (from 176 to 745 classes). Conclusion: The study proposes an SDP model based OANFIS that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. OANFIS is a novel approach that uses the flexibility provided by the ANFIS model and optimizes the same using PSO. The results given by OANFIS are very good and it can also be concluded that the performance of the SDP model based on OANFIS might be influenced by the size of projects. Discussion: The performance of the SDP model based on OANFIS is better than the ANFIS model. It can also be concluded that the performance of the SDP model might be influenced by the size of projects.

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