A Bidirectional Reasoning Based on Fuzzy Interpolation

2020 ◽  
Vol 12 (1) ◽  
pp. 1-14
Author(s):  
Shangzhu Jin
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Curse Of Dimensionality ◽  
Rule Base ◽  
Fuzzy Interpolation ◽  
Initial Idea ◽  
Conventional Methods ◽  
Hierarchical Fuzzy Systems

In order to deal with both the “curse of dimensionality” and the “sparse rule base” simultaneously, an initial idea of hierarchical bidirectional fuzzy interpolation is presented in this article, combining hierarchical fuzzy systems and forward/backward fuzzy rule interpolation. In particular, backward fuzzy interpolation can be employed to allow interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. Hierarchical bidirectional fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and any sub-layer rule base is sparse. The implementation of this approach is based on fuzzy rule interpolative reasoning that utilities scale and move transformation. An illustrative example and application scenario are provided to demonstrate the efficacy of this proposed approach.

Hierarchical Fuzzy Rule Interpolation and its Application for Hotels Location Selection

Fuzzy Systems ◽  
2017 ◽  
pp. 31-54
Author(s):  
Yanling Jiang ◽  
Shangzhu Jin ◽  
Jun Peng
Keyword(s):  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Curse Of Dimensionality ◽  
Fuzzy Decision Making ◽  
Rule Base ◽  
Location Selection ◽  
Fuzzy Interpolation ◽  
Practical Applications ◽  
International Tourist ◽  
Hierarchical Fuzzy Systems

Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in systems of only a sparse rule base. However in practical applications, as the application domain of fuzzy systems expand to more complex ones, the curse of dimensionality problem of the conventional fuzzy systems became apparent, which makes the already challenging tasks such as inference and interpolation even more difficult. An initial idea of hierarchical fuzzy interpolation is presented in this paper. The proposed approach combines hierarchical fuzzy systems and fuzzy rule interpolation, to overcome the curse of dimensionality problem and the sparse rule base problem simultaneously. Hierarchical fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and the sub-layer rules base is sparse. In order to demonstrate the potential of this approach, a hierarchical fuzzy decision making model for international tourist hotel location selection is provided in this paper. Criteria are acquired from literatures review and practical investigations for selecting the international tourist hotel location. These supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel, where lack enough information about the important indicators and factors. This model can also support the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. An empirical study for identifying the international tourist hotel location selection in Chongqing is conducted to demonstrate the computational results and effectiveness of the proposed methodology.

Hierarchical Fuzzy Rule Interpolation and its Application for Hotels Location Selection

2016 ◽  
Vol 10 (1) ◽  
pp. 55-79 ◽  
Author(s):  
Yanling Jiang ◽  
Shangzhu Jin ◽  
Jun Peng
Keyword(s):  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Practical Investigations ◽  
Curse Of Dimensionality ◽  
Fuzzy Decision Making ◽  
Rule Base ◽  
Location Selection ◽  
Fuzzy Interpolation ◽  
Practical Applications ◽  
International Tourist

Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in systems of only a sparse rule base. However in practical applications, as the application domain of fuzzy systems expand to more complex ones, the curse of dimensionality problem of the conventional fuzzy systems became apparent, which makes the already challenging tasks such as inference and interpolation even more difficult. An initial idea of hierarchical fuzzy interpolation is presented in this paper. The proposed approach combines hierarchical fuzzy systems and fuzzy rule interpolation, to overcome the curse of dimensionality problem and the sparse rule base problem simultaneously. Hierarchical fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and the sub-layer rules base is sparse. In order to demonstrate the potential of this approach, a hierarchical fuzzy decision making model for international tourist hotel location selection is provided in this paper. Criteria are acquired from literatures review and practical investigations for selecting the international tourist hotel location. These supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel, where lack enough information about the important indicators and factors. This model can also support the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. An empirical study for identifying the international tourist hotel location selection in Chongqing is conducted to demonstrate the computational results and effectiveness of the proposed methodology.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

A Novel Fuzzy Rule Guided Intelligent Technique for Gray Image Extraction and Segmentation

2013 ◽  
pp. 163-181 ◽  
Author(s):  
Koushik Mondal
Keyword(s):  
Fuzzy Systems ◽  
Domain Knowledge ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Fuzzy Rule ◽  
Absolute Error ◽  
Rule Base ◽  
Process Uncertainty ◽  
Ann Models ◽  
Artificial Neural Network Ann

Image segmentation and subsequent extraction from a noise-affected background, has all along remained a challenging task in the field of image processing. There are various methods reported in the literature to this effect. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, et cetera. Providing an extraction solution working in unsupervised mode happens to be even more interesting a problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that effort in this respect appears to be quite rudimentary. This chapter proposes a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, the author takes recourse to effective metrices like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).

Analysis and Application of a Kind of Hierarchical Fuzzy Systems

2011 ◽  
Vol 219-220 ◽  
pp. 1097-1100 ◽  
Author(s):  
Jie Wang ◽  
Xiao Dong Zhu
Keyword(s):  
Structural Analysis ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Weight Coefficient ◽  
Oil Drilling ◽  
Analytic Hierarchical Process ◽  
Input Variables ◽  
Hierarchical Fuzzy System ◽  
Hierarchical Fuzzy Systems ◽  
Forewarning System

In this paper a kind of hierarchical fuzzy systems was introduced. The characteristics and structural relation of this hierarchical fuzzy system were analyzed. The sensitivity between the input variables and the output variables and the position of variables in the hierarchical fuzzy system were given according to the importance of variables. The weight coefficient of variables was confirmed applying the methods of analytic hierarchical process (AHP). Then the structural analysis and the weight coefficient were applied to the forewarning system of oil drilling.

scholarly journals Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Ferdinando Di Martino ◽  
Vincenzo Loia ◽  
Salvatore Sessa
Keyword(s):  
Spatial Analysis ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Similarity Index ◽  
Fuzzy Rule ◽  
Hierarchical Cluster ◽  
Geographical Information ◽  
Swarm Optimization ◽  
Rule Set ◽  
Takagi Sugeno

We present a method by using the hierarchical cluster-based Multispecies particle swarm optimization to generate a fuzzy system of Takagi-Sugeno-Kang type encapsulated in a geographical information system considered as environmental decision support for spatial analysis. We consider a spatial area partitioned in subzones: the data measured in each subzone are used to extract a fuzzy rule set of above mentioned type. We adopt a similarity index (greater than a specific threshold) for comparing fuzzy systems generated for adjacent subzones.

scholarly journals Brain Storm Optimization Algorithm for Data Classification

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Shaolin Zhu ◽  
Tianqi Li ◽  
Baohuan Li
Keyword(s):  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Inductive Learning ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Search Space ◽  
Exponential Model ◽  
Brain Storm Optimization ◽  
The Brain

Abstract With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

A Novel Fuzzy Rule Guided Intelligent Technique for Gray Image Extraction and Segmentation

2013 ◽  
pp. 303-321
Author(s):  
Koushik Mondal
Keyword(s):  
Fuzzy Systems ◽  
Domain Knowledge ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Fuzzy Rule ◽  
Absolute Error ◽  
Rule Base ◽  
Process Uncertainty ◽  
Ann Models ◽  
Artificial Neural Network Ann

Image segmentation and subsequent extraction from a noise-affected background, has all along remained a challenging task in the field of image processing. There are various methods reported in the literature to this effect. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, et cetera. Providing an extraction solution working in unsupervised mode happens to be even more interesting a problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that effort in this respect appears to be quite rudimentary. This chapter proposes a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, the author takes recourse to effective metrices like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).

Inference Based on α-Cut and Generalized Mean in Representing Fuzzy-Valued Functions

2010 ◽  
Vol 14 (6) ◽  
pp. 581-592 ◽  
Author(s):  
Kiyohiko Uehara ◽  
◽  
Takumi Koyama ◽  
Kaoru Hirota ◽  
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Mean Square ◽  
Generalized Mean ◽  
Simulation Results ◽  
Conventional Methods ◽  
The Difference ◽  
Mean Square Errors

It is mathematically proved that inference based on α-cuts and generalized mean (α-GEMII) deduces consequences converging to fuzzy sets mapped by linear fuzzy-valued functions, to be represented with α-GEMII, as the number of fuzzy rules increases. The proof indicates that α-GEMII satisfies axiomatic properties and can contribute to presenting interpretability in designing fuzzy systems in the rule base. Such properties do not hold in conventional methods based on the compositional rule of inference. Simulation results show that the difference between deduced consequences and fuzzy sets mapped by linear fuzzyvalued functions is smaller as the number of fuzzy rules increases, in which the difference is evaluated by mean square errors. The discussions may lead to improvements of the interpretability in representing nonlinear fuzzy-valued functions by using α-GEMII.

Co-Evolution of Fuzzy Controller for the Mobile Robot Control

2004 ◽  
Vol 8 (4) ◽  
pp. 356-361 ◽  
Author(s):  
Kwang-Sub Byun ◽  
◽  
Chang-Hyun Park ◽  
Kwee-Bo Sim
Keyword(s):  
Mobile Robot ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Membership Functions ◽  
Mobile Robot Control ◽  
Evolution Algorithms

In this paper, we design the fuzzy rules using a modified Nash Genetic Algorithm. Fuzzy rules consist of antecedents and consequents. Because this paper uses the simplified method of Sugeno for the fuzzy inference engine, consequents have not membership functions but constants. Therefore, each fuzzy rule in this paper consists of a membership function in the antecedent and a constant value in the consequent. The main problem in fuzzy systems is how to design the fuzzy rule base. Modified Nash GA coevolves membership functions and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the design of the fuzzy controller for a mobile robot. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm.

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