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.

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.

scholarly journals A Pre-Trained Fuzzy Reinforcement Learning Method for the Pursuing Satellite in a One-to-One Game in Space

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2253
Author(s):  
Xiao Wang ◽  
Peng Shi ◽  
Yushan Zhao ◽  
Yue Sun
Keyword(s):  
Reinforcement Learning ◽  
Gradient Descent ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Control Policy ◽  
Descent Method ◽  
Gradient Descent Method ◽  
One To One ◽  
Inference Systems ◽  
First Time

In order to help the pursuer find its advantaged control policy in a one-to-one game in space, this paper proposes an innovative pre-trained fuzzy reinforcement learning algorithm, which is conducted in the x, y, and z channels separately. Compared with the previous algorithms applied in ground games, this is the first time reinforcement learning has been introduced to help the pursuer in space optimize its control policy. The known part of the environment is utilized to help the pursuer pre-train its consequent set before learning. An actor-critic framework is built in each moving channel of the pursuer. The consequent set of the pursuer is updated through the gradient descent method in fuzzy inference systems. The numerical experimental results validate the effectiveness of the proposed algorithm in improving the game ability of the pursuer.

Wireless Brain Wave Classification for Alzheimer’s Patients via Efficient Neural Network Computation

2018 ◽  
Vol 10 (03) ◽  
pp. 1850004
Author(s):  
Grant Sheen
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Normal Person ◽  
Gradient Descent Method ◽  
Step Size ◽  
Brain Wave ◽  
Proposed Model ◽  
Wireless Recording

Wireless recording and real time classification of brain waves are essential steps towards future wearable devices to assist Alzheimer’s patients in conveying their thoughts. This work is concerned with efficient computation of a dimension-reduced neural network (NN) model on Alzheimer’s patient data recorded by a wireless headset. Due to much fewer sensors in wireless recording than the number of electrodes in a traditional wired cap and shorter attention span of an Alzheimer’s patient than a normal person, the data is much more restrictive than is typical in neural robotics and mind-controlled games. To overcome this challenge, an alternating minimization (AM) method is developed for network training. AM minimizes a nonsmooth and nonconvex objective function one variable at a time while fixing the rest. The sub-problem for each variable is piecewise convex with a finite number of minima. The overall iterative AM method is descending and free of step size (learning parameter) in the standard gradient descent method. The proposed model, trained by the AM method, significantly outperforms the standard NN model trained by the stochastic gradient descent method in classifying four daily thoughts, reaching accuracies around 90% for Alzheimer’s patient. Curved decision boundaries of the proposed model with multiple hidden neurons are found analytically to establish the nonlinear nature of the classification.

An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization

2016 ◽  
Vol 1 (1) ◽  
pp. 201 ◽  
Author(s):  
Lei Meng ◽  
Shoulin Yin ◽  
Xinyuan Hu
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Gradient Descent ◽  
Fuzzy Rule ◽  
Pso Algorithm ◽  
Fuzzy Neural Networks ◽  
Descent Method ◽  
Local Optimum ◽  
Gradient Descent Method ◽  
Fuzzy Neural

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.

Linear Regression Method for Review Aspect Rating Prediction

2014 ◽  
Vol 543-547 ◽  
pp. 3507-3510
Author(s):  
Lan You ◽  
Qing Xi Peng
Keyword(s):  
Linear Regression ◽  
Gradient Descent ◽  
Online Reviews ◽  
Descent Method ◽  
Coarse Grained ◽  
Experimental Result ◽  
Linear Regression Method ◽  
Gradient Descent Method ◽  
Proposed Model ◽  
Potential Customers

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. The reviewers usually give a whole rating score to the product. The potential customers tend to make decision according to the reviews. Previous works mainly focus on the summarization of the rating and sentiment of reviews. However, they ignore an important question. The whole rating can be regarded as linear regression of different aspect ratings. High aspect rating and low aspect rating compensate each other. Therefore, previous works are coarse-grained analysis. This paper first proposed a weak supervised learning method to extract implicit aspect with aspect seeds. It then formulates the aspect rating problem as a linear regression model. Finally a gradient descent method is proposed to handle the problem. Different datasets are collected. Experimental result in the datasets demonstrates the advantage of the proposed model.

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