Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method

2018 ◽  
Vol 22 (2) ◽  
pp. 172-175
Author(s):  
Takeshi Nagata ◽  
Hirosato Seki ◽  
Hiroaki Ishii ◽  
◽  
◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Simplex Method ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Inference Model ◽  
Two Phase ◽  
Inference Models ◽  
Learning Rules ◽  
Proposed Model ◽  
Single Input

Single Input Rule Modules connected fuzzy inference model (SIRMs model, for short) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. However, it is difficult to understand the meaning of the weight for the SIRMs model because the value of the weight has no restriction in the learning rules. Therefore, the paper proposes a constrained SIRMs model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed model by applying it to a medical diagnosis.

SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model

1997 ◽  
Vol 1 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Naoyoshi Yubazaki ◽  
◽  
Jianqiang Yi ◽  
Kaoru Hirota ◽  
Keyword(s):  
Gradient Descent ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Control Performance ◽  
Nonlinear Functions ◽  
Inference Model ◽  
Proposed Model ◽  
Single Input

A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical secondorder lag systems. The simulation results show that the proposed model can largely improve the control performance compared with that of the conventional fuzzy inference model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed model also has the ability to identify nonlinear systems.

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.

On the Learning Method, Properties of the Extended Functional-Type SIRMs Connected Fuzzy Inference Model and Their Application to a Medical Diagnosis System

2018 ◽  
Vol 22 (2) ◽  
pp. 176-183
Author(s):  
Diederik van Krieken ◽  
Hirosato Seki ◽  
Masahiro Inuiguchi ◽  
◽  
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Inference ◽  
Learning Method ◽  
Functional Type ◽  
Inference Model ◽  
Diagnosis System ◽  
Single Input ◽  
Medical Diagnosis System

Seki et al. have proposed the functional type single input rule modules fuzzy inference model (functional-type SIRMs model, for short) which generalized consequent part of SIRMs model to function. However, it is too strict to satisfy the equivaence conditions of T–S inference model. Therefore, this paper proposes an extended functional-type SIRMs model (EF-SIRMs, for short) in which the consequent part of the functional-type SIRMs model is extended to a function with 1 dimensional polynomial from a function with n dimensional polynomial, and its properties are clarified. Further, it shows the ability of this model becomes greatly larger than that of ordinary functional-type SIRMs model. Moreover, it proposes a learning method of the EF-SIRMs model, and it is applied to a medical diagnosis, and compared with the conventional SIRMs models.

A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model

2001 ◽  
Vol 5 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Jianqiang Yi ◽  
◽  
Naoyoshi Yubazaki ◽  
Kaoru Hirota ◽  
◽  
...  
Keyword(s):  
Position Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Initial Position ◽  
Initial Angle ◽  
Inference Model ◽  
Stabilization Control ◽  
Control Situations ◽  
Single Input ◽  
Simulation Results

A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

scholarly journals Learning rules for a fuzzy inference model

1993 ◽  
Vol 59 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Luis M. de Campos ◽  
Serafín Moral
Keyword(s):  
Fuzzy Inference ◽  
Inference Model ◽  
Learning Rules

A FUZZY NEURAL NETWORK MODEL FOR FUZZY INFERENCE AND RULE TUNING

1994 ◽  
Vol 02 (03) ◽  
pp. 265-277 ◽  
Author(s):  
KEON-MYUNG LEE ◽  
DONG-HOON KWANG ◽  
HYUNG LEEK WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Target System ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Fine Tune

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

THE INTEGRATION OF EXTENSION THEORY TO DESIGN A NEW FUZZY INFERENCE MODEL

2000 ◽  
Vol 09 (04) ◽  
pp. 473-492 ◽  
Author(s):  
YO-PING HUANG ◽  
HUNG-JIN CHEN ◽  
CHI-PENG OUYANG
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Extension Theory ◽  
Turning Process ◽  
Inference Model ◽  
Single Input Single Output ◽  
Identification Process ◽  
Single Output ◽  
System Requirement ◽  
Single Input

A novel extension-based fuzzy model is proposed in this paper. The newly established extension theory is integrated into the conventional fuzzy system to enhance the reasoning capability. In parameter identification process, adjusting a membership function to satisfy one pattern may deteriorate the others performance and result in a lengthy turning process. This incompatible issue is alleviated by the extension theory. We will investigate how to define the extended relational functions and how to refine the roughly designed model to meet the system requirement. During the refining process, both the fired and the neighborhood of the fired membership functions are adjusted simultaneously. Simulation results from single-input-single-output and double-input-single-output models verified that better results than the conventional methods have been obtained.

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