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.

Rule Reduction and Robust Control of Generalized Takagi-Sugeno Fuzzy Systems

2000 ◽  
Vol 4 (5) ◽  
pp. 373-379 ◽  
Author(s):  
Tadanari Taniguchi ◽  
◽  
Kazuo Tanaka ◽  
Keyword(s):  
Robust Control ◽  
Model Reduction ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Controller Design ◽  
Main Idea ◽  
Linear Matrix ◽  
Robust Controller ◽  
Reduction Error ◽  
Takagi Sugeno

This paper presents model reduction and robust control using a generalized form of Takagi-Sugeno fuzzy systems. We first define a generalized form of TakagiSugeno fuzzy systems. The generalized form has a decomposed structure for each element of <I>Ai</I> and <I>Bi</I> matrices in consequent parts. The key feature of this structure is that it is suitable for reducing the number of rules. Conditions to reduce the number of rules are represented in terms of linear matrix inequality (LMIs). The main idea is to find a structure of if-then rules of the reduced model that agrees well with dynamics of the original model. Furthermore, we estimate the lower bound of the norm of model uncertainty of the Takagi-Sugeno fuzzy system that can cover the reduction error. Finally, we present an example of model reduction and robust control for a nonlinear system. In this example, we achieve a robust controller design to compensate for the uncertainly of the Takagi-Sugeno fuzzy system.

scholarly journals Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

2019 ◽  
Vol 22 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski
Keyword(s):  
Particle Swarm Optimization ◽  
Least Squares ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Piecewise Linear ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Takagi Sugeno

AbstractThis paper proposes two methods for training Takagi–Sugeno (T-S) fuzzy systems using batch least squares (BLS) and particle swarm optimization (PSO). The T-S system is considered with triangular and Gaussian membership functions in the antecedents and higher-order polynomials in the consequents of fuzzy rules. In the first method, the BLS determines the polynomials in a system in which the fuzzy sets are known. In the second method, the PSO algorithm determines the fuzzy sets, whereas the BLS determines the polynomials. In this paper, the ridge regression is used to stabilize the solution when the problem is close to the singularity. Thanks to this, the proposed methods can be applied when the number of observations is less than the number of predictors. Moreover, the leave-one-out cross-validation is used to avoid overfitting and this way to choose the structure of a fuzzy model. A method of obtaining piecewise linear regression by means of the zero-order T-S system is also presented.

scholarly journals The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 3357-3362
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rule ◽  
Training Data ◽  
Subtractive Clustering ◽  
A Value ◽  
Testing Data ◽  
System Input ◽  
Input Variables ◽  
Rule Bases ◽  
Takagi Sugeno

The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input including . The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules.The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.

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 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.

Stability of a Class of Discrete-Time Switched Fuzzy Systems by Arbitrary Switching

2014 ◽  
Vol 536-537 ◽  
pp. 1187-1190
Author(s):  
Hong Yang ◽  
Huan Huan Lü ◽  
Le Zhang
Keyword(s):  
Discrete Time ◽  
Fuzzy System ◽  
Lyapunov Functions ◽  
Fuzzy Systems ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Simulation Experiments ◽  
Arbitrary Switching ◽  
Lyapunov Functions Method ◽  
Takagi Sugeno

This paper investigates a representation model, namely, a discrete-time switched fuzzy system. In this model, a system is a switched system whose subsystems are all discrete-time Takagi-Sugeno (T-S) fuzzy systems. For the proposed discrete-time switched fuzzy system is built to ensure that the relevant system is asymptotically stable by Arbitrary Switching and the Lyapunov functions method. The main conditions are given in form of linear matrix inequalities (LMIs), which are easily solvable. The elaborated illustrative examples and the respective simulation experiments demonstrate the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document