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Construction of fuzzy inference rules by NDF and NDFL

Isao Hayashi, Hiroyoshi Nomura, Hisayo Yamasaki, Noboru Wakami
1992 International Journal of Approximate Reasoning  
In an NDF algorithm the optimum membership function in the antecedent part of fuzzy inference rules is determined by a neural network, while in the consequent parts an amount of reasoning for each rule  ...  On the other hand, we propose a new algorithm that can adjust inference rules to compensate for a change of inference environment.  ...  This means that an input space consisting of x~ and x 2 is divided into individual partial spaces by a number of fuzzy rules, and the fuzzy sets of the antecedent of each inference rule are constructed  ... 
doi:10.1016/0888-613x(92)90019-v fatcat:nyaudmy44ngtxanf6faebphdxi

Fuzzy Rule Based Inference System for Implementation of Naval Military Mission

Rashmi Singh, Vipin Saxena
2018 International Journal of Computer Network and Information Security  
of a human trained by means of a rule-based inference system.  ...  The present system expects to help the choice about changing a unit to a mission considering that ambiguity and unpredictability of information by methods of fuzzy concepts and imitates the selection procedure  ...  classes and proposed the use of hierarchical fuzzy rule base classification system i.e. based on the refinement of a simple linguistic fuzzy method by using the extension of the structure of the knowledge  ... 
doi:10.5815/ijcnis.2018.04.04 fatcat:rtzgd6oduzfv3d35ctagen2iwa

A Survey on Applications of Adaptive Neuro Fuzzy Inference System

Navneet Walia, Sharad Kumar, Harsukhpreet Singh
2015 International Journal of Hybrid Information Technology  
In this paper we presented an architecture and basic learning process underlying in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of  ...  This study involves study of ANFIS strategy ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time  ...  Sharad Tiwari, Research Scholar, Thapar University, Patiala and anonymous reviewers for their invaluable suggestions that greatly help to improve the quality of paper.  ... 
doi:10.14257/ijhit.2015.8.11.30 fatcat:oycbgs7p6zhzhkokojq35pshwq

Fuzzy logic based approaches for gene regulatory network inference [article]

Khalid Raza
2018 arXiv   pre-print
As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing.  ...  From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data.  ...  In this step, a rule base in the form of "IF-THEN" rule is constructed in order to control the output variable. inference engine is to draw conclusions from rule base.  ... 
arXiv:1804.10775v1 fatcat:ct4yxzdq45ebdobxdsuwb3zlmy

A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System

Tharwat O.S.Hanafy
2014 International Journal of Computer Applications  
By contrast, a fuzzy inference system employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning process without employing precise quantitative analyses.  ...  The software of the modified algorithm of MIMO model identification is built and generated by me or added as a toolbox to matlab.  ...  Adaptive network fuzzy inference systems To illustrate the use of neural networks for fuzzy inference, we present some successful adaptive neural network fuzzy inference systems, along with training algorithm  ... 
doi:10.5120/16450-6066 fatcat:b3bffavpqbf23ik5yklxlaa2ge

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

Tuqyah Abdullah Al Qazlan, Aboubekeur Hamdi-Cherif, Chafia Kara-Mohamed
2015 The Scientific World Journal  
To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference.  ...  The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.  ...  Acknowledgment This work is supported by the Deanship of Scientific Research at Qassim University, Saudi Arabia, as part of the research project with ID no. 1223-2012.  ... 
doi:10.1155/2015/148010 pmid:25879048 pmcid:PMC4386676 fatcat:kjdq6dmcwre4rp7vp2ye3hmeja

Adaptation of Fuzzy Inference System Using Neural Learning [chapter]

A. Abraham
2005 Studies in Fuzziness and Soft Computing  
The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models.  ...  Different Mamdani and Takagi-Sugeno type integrated neuro-fuzzy systems are further introduced with a focus on some of the salient features and advantages of the different types of integrated neuro-fuzzy  ...  • Construction of fuzzy rule base Generation of new input cluster corresponds to the generation of a new fuzzy rule, with its precondition part constructed by the learning algorithm in process.  ... 
doi:10.1007/11339366_3 fatcat:hyt6m7zr3bfnxjv24bxrwkz6la

FuzzyGuard: A DDoS attack prevention extension in software-defined wireless sensor networks

2019 KSII Transactions on Internet and Information Systems  
Then, the attack detection is implemented by fuzzy inference method to output the current security state of the network.  ...  In FuzzyGuard, a control network with both the protection of data flow and the convergence of attack flow is constructed in the data plane by using the idea of independent routing control flow.  ...  implemented by a fuzzy inference system consisting of four modules: fuzzification, fuzzy inference, defuzzification, and rule-base.  ... 
doi:10.3837/tiis.2019.07.019 fatcat:kbe6xy2565di3gq7eb2t2lx7tu

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

Navneet Walia, Harsukhpreet Singh, Anurag Sharma
2015 International Journal of Computer Applications  
In this paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of  ...  Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm.  ...  This study involves fuzzy inference system implemented in the construction of adaptive networks.  ... 
doi:10.5120/ijca2015905635 fatcat:uljjwrc35nemrjvlsv2erdstcy

Flatness Intelligent Control Based on T-S Cloud Inference Neural Network

Xiuling Zhang, Liang Zhao, Jiayin Zang, Hongmin Fan, Long Cheng
2014 ISIJ International  
In this paper, T-S cloud inference neural network and its stability are proposed. It is constructed by cloud model and T-S fuzzy neural network.  ...  The stability of T-S cloud inference neural network is analyzed by Lyapunov method in details. Based on the new network, flatness recognition model and flatness predictive model are established.  ...  This work is supported by the National Natural Science Foundation of China (Grants No. 61074099) and Cultivation Program Project for Leading Talent of Innovation Team in Colleges and Universities of Hebei  ... 
doi:10.2355/isijinternational.54.2608 fatcat:mwd2hrtghjhh5pekwmvjdjwabq

A DYNAMIC TEMPORAL NEURO FUZZY INFERENCE SYSTEM FOR MINING MEDICAL DATABASES

Nadim
2012 Journal of Computer Science  
This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cognetive maps into a fuzzy temporal rule based inference  ...  For this purpose, a four-layer fuzzy temporal neural network is proposed and implemented by the automatic creation of the conventional FTCMs from the given data.  ...  The FTCM is then used for fuzzy temporal reasoning by converting it into a rule based fuzzy inference system.  ... 
doi:10.3844/jcssp.2012.1924.1931 fatcat:buuf6b3zkrhubmlvovnec7dtty

A neurofuzzy network knowledge extraction and extended gram-schmidt algorithm for model subspace decomposition

Xia Hong, C.J. Harris
2003 IEEE transactions on fuzzy systems  
The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base.  ...  The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism.  ...  A functional inference of a fuzzy rule as a matrix feature subspace is introduced based on an extension of the T-S inference mechanism to achieve a rule-based neurofuzzy system with exceptional rule extraction  ... 
doi:10.1109/tfuzz.2003.814842 fatcat:jx4zimhvwbfblpj3637zipx5pm

KNOWLEDGE-ORIENTED TECHNOLOGIES IN HIGHLY AUTOMATED PRODUCTION

Ye. I. Kucherenko, S. N. Trokhimchuk, O. D. Driuk
2014 Radìoelektronika, Ìnformatika, Upravlìnnâ  
The expansion of Zadeh-Mamdani method in problems of fuzzy inference on knowledge is considered. A modified method of fuzzy inference is proposed and justified.  ...  The proposed method is based on interpretation of components of fuzzy Petri nets as production rules and solving of logical equations in the state space of membership functions of the model, followed by  ...  justify a modified method of fuzzy inference, based on interpretation of the components of fuzzy PN by production rules, solving the logical equations in a state space of membership functions and classifying  ... 
doi:10.15588/1607-3274-2014-2-12 fatcat:wc7d4usnpng2jfprxfmnofqks4

The Idea of Knowledge Supplementation and Explanation Using Neural Networks to Support Decisions in Construction Engineering

Marcin Gajzler
2013 Procedia Engineering  
In order to ensure more completeness of the knowledge and explain the mechanism of inference, the KBANN (Knowledge Based Artificial Neural Network) algorithm was used, which enables extracting rules that  ...  are not a part of the original state of knowledge using trained neural networks.  ...  One of them, proposed by the author in [6] , [16] , is the method of increasing the population of cases and fuzzy rules by using "shifts" of the center of gravity of the fuzzy set.  ... 
doi:10.1016/j.proeng.2013.04.041 fatcat:45qbgr7ku5ds3foxvmcgetzvl4

Attempt of adaptive neuro-fuzzy inference system(ANFIS)for ultrasonographic diagnosis

Takemasa Tanaka, Shigenobu Kanda
2000 International journal of biomedical soft computing and human sciences  
The authors wilI apply ANFIS for various types ofcomputer-aided diagnosis in the future.  ...  The outline of the diagnostic rule is invisible and encapsulated, Adaptive neuro-fuzzy inference system (ANFIS) is one of the fuzzy inference methods, which is incorporated wi{h a concept of neural network  ...  (Fig. 3-b) ANFIS can be recognized as an extension of fuzzy inference.  ... 
doi:10.24466/ijbschs.6.1_69 fatcat:l5rlxh3xzfcehnc3khgnaymbca
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