Parallel Evolutionary Asymmetric Subsethood Product Fuzzy-Neural Inference System: An Island Model Approach

2007 ◽  
Author(s):  
Lotika Singh ◽  
Satish Kumar
Keyword(s):  
Island Model ◽  
Inference System ◽  
Fuzzy Neural ◽  
Model Approach

Research on Neural Networks Used in Parallel Computing

2013 ◽  
Vol 411-414 ◽  
pp. 1998-2001
Author(s):  
Liang Cheng
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Paper Study ◽  
Optimal Solutions ◽  
Inference System ◽  
Iterative Processing ◽  
Computational Environment ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Expected Increase

The parallel programming approaches were in the focus of research efforts due to an expected increase in efficiency of iterative processing in the parallel computational environment. On this end the parallel evolutionary asymmetric subset-hood product fuzzy-neural inference system has been developed to take advantage of parallelization in message passing. This paper study the structure of the neural network and the time series forecasting with neural network, the results could help us to obtain the optimal solutions to higher complexity of the problem.

Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems

2011 ◽  
Vol 332-334 ◽  
pp. 1505-1510
Author(s):  
Xiao Bo Yang
Keyword(s):  
Network Inference ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Real Data ◽  
Subtractive Clustering ◽  
Fuzzy Inference Systems ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Neural ◽  
Inference Systems

In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability of the proposed method to real data.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

scholarly journals APPLICATION OF NEURO-FUZZY MODELS IN THE SYSTEM FOR ASSESSING THE PROFESSIONAL ABILITIES OF APPLICANTS

2021 ◽  
pp. 107-113
Author(s):  
Yuliia Riabchun ◽  
Roman Skrypak ◽  
Olena Riabchun ◽  
Iryna Aznaurian
Keyword(s):  
Higher Education ◽  
Structural Model ◽  
Fuzzy Inference ◽  
Higher Education Institutions ◽  
A Priori ◽  
Education Institution ◽  
Inference System ◽  
Intellectual System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The work is devoted to solving a problem of assessing the professional abilities of entrants to higher education institutions. The subject of the study is the process of automatic support of entrants' decisions in conditions of fuzzy uncertainty caused by the need to communicate "online". The object of the study is a supporting means of the decisions of applicants to choose the direction of study "online". The main purpose of the work is to substantiate the technology of decision support for choosing a direction of study using an infocommunication system, the work of which has based on a neuro-fuzzy output system. Particular attention has paid to overcoming the problems that accompany the creation of infocommunication systems, which has designed to support decision-making on the choice of field of study in conditions of unclear uncertainty caused by the limitation of offline communication. The article presents the results of a study of the criteria for admission to higher education institutions in different countries. The structural model of the Specialized Intellectual System of Identification of Abilities of Entrants has offered. The system has designed to support the decision to choose a specialty of higher education institution. It has shown that to substantiate the recommendation conclusion based on the results of professional game; it is advisable to use a fuzzy neural network Takagi-Sugeno-Kanga. To solve the problem of substantiation of expert decisions at the stage of formation of a priori base of rules of fuzzy knowledge base of fuzzy inference system, it is expedient to use Mamdani model, which operates with linguistic variables and fuzzy sets.

Hybrid Fuzzy Neural Search Retrieval System

2016 ◽  
Vol 12 (3) ◽  
pp. 78-93 ◽  
Author(s):  
Rawan Ghnemat ◽  
Adnan Shaout
Keyword(s):  
Fuzzy Inference System ◽  
Search Engines ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Experimental System ◽  
Search Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Face Search

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

Hybrid Fuzzy Neural Search Retrieval System

Fuzzy Systems ◽  
2017 ◽  
pp. 443-458 ◽  
Author(s):  
Rawan Ghnemat ◽  
Adnan Shaout
Keyword(s):  
Fuzzy Inference System ◽  
Search Engines ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Experimental System ◽  
Search Process ◽  
Inference System ◽  
Fuzzy Neural ◽  
Hybrid Intelligent System ◽  
Face Search

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

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