scholarly journals Artificial Hydrocarbon Networks Fuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hiram Ponce ◽  
Pedro Ponce ◽  
Arturo Molina
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Model ◽  
Inference System ◽  
Position Controller ◽  
Inference Systems ◽  
Linguistic Units ◽  
Output Space

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.

SCALABLE ARCHITECTURE FOR HIGH-SPEED MULTIDIMENSIONAL FUZZY INFERENCE SYSTEMS

2011 ◽  
Vol 20 (03) ◽  
pp. 375-400 ◽  
Author(s):  
INÉS DEL CAMPO ◽  
JAVIER ECHANOBE ◽  
KOLDO BASTERRETXEA ◽  
GUILLERMO BOSQUE
Keyword(s):  
High Speed ◽  
High Performance ◽  
Fuzzy Inference ◽  
Reconfigurable Hardware ◽  
Rule Base ◽  
Fuzzy Inference Systems ◽  
Scalable Architecture ◽  
Inference Model ◽  
Inference System ◽  
Inference Systems

This paper presents a scalable architecture suitable for the implementation of high-speed fuzzy inference systems on reconfigurable hardware. The main features of the proposed architecture, based on the Takagi–Sugeno inference model, are scalability, high performance, and flexibility. A scalable fuzzy inference system (FIS) must be efficient and practical when applied to complex situations, such as multidimensional problems with a large number of membership functions and a large rule base. Several current application areas of fuzzy computation require such enhanced capabilities to deal with real-time problems (e.g., robotics, automotive control, etc.). Scalability and high performance of the proposed solution have been achieved by exploiting the inherent parallelism of the inference model, while flexibility has been obtained by applying hardware/software codesign techniques to reconfigurable hardware. Last generation reconfigurable technologies, particularly field programmable gate arrays (FPGAs), make it possible to implement the whole embedded FIS (e.g., processor core, memory blocks, peripherals, and specific hardware for fuzzy inference) on a single chip with the consequent savings in size, cost, and power consumption. As a prototyping example, we implemented a complex fuzzy controller for a vehicle semi-active suspension system composed of four three-input FIS on a single FPGA of the Xilinx's Virtex 5 device family.

An approach for non-singleton generalized Type-2 fuzzy classifiers

2020 ◽  
Vol 39 (5) ◽  
pp. 7203-7215
Author(s):  
Emanuel Ontiveros-Robles ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Systems ◽  
General Type ◽  
Fuzzy Inference ◽  
Classification Problems ◽  
Fuzzy Inference Systems ◽  
Classification Rate ◽  
Inference System ◽  
Fuzzy Classifiers ◽  
Inference Systems

In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts.

scholarly journals On the Monotonicity of Fuzzy Inference Models

2012 ◽  
Vol 16 (5) ◽  
pp. 592-602 ◽  
Author(s):  
Hirosato Seki ◽  
◽  
Kai Meng Tay ◽  
Keyword(s):  
Fuzzy Inference ◽  
Monotonicity Property ◽  
Fuzzy Inference Systems ◽  
Inference Model ◽  
Inference System ◽  
Inference Models ◽  
Input Type ◽  
Single Input ◽  
Monotonicity Conditions ◽  
Inference Systems

Monotonicity property is very important in real systems. The monotonicity may need to be satisfied in a variety of application domains, e.g., control, medical diagnosis, educational evaluation, etc. A search in the literature reveals that the importance of the monotonicity in fuzzy inference system has been highlighted. Therefore, this paper surveys the works relating the monotonicity for various fuzzy inference systems. It firstly focuses on the monotonicity of the Mamdani inference model. Themonotonicity ofMamdani model is shown by using a defuzzification method in cases of three t-norms. Secondly, the monotonicity conditions and applications of the T–S inference model are stated. Finally, the monotonicity of the single input type fuzzy inference models is surveyed.

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