Simplified Fuzzy Reasoning Research Articles

New Fusion Algorithm-Reinforced Pilot Control for an Agricultural Tricopter UAV

Currently, fuzzy proportional integral derivative (PID) controller schemes, which include simplified fuzzy reasoning decision methodologies and PID parameters, are broadly and efficaciously practiced in various fields from industrial applications, military service, to rescue operations, civilian information and also horticultural observation and agricultural surveillance. A fusion particle swarm optimization (PSO)–evolutionary programming (EP) algorithm, which is an improved version of the stochastic optimization strategy PSO, was presented for designing and optimizing controller gains in this study. The mathematical calculations of this study include the reproduction of EP with PSO. By minimizing the integral of the absolute error (IAE) criterion that is used for estimating the system response as a fitness function, the obtained integrated design of the fusion PSO–EP algorithm generated and updated the new elite parameters for proposed controller schemes. This progression was used for the complicated non-linear systems of the attitude-control pilot models of a tricopter unmanned aerial vehicle (UAV) to demonstrate an improvement on the performance in terms of rapid response, precision, reliability, and stability.

Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

Neurorehabilitation using a brain–computer interface (BCI) requires machine learning, for which calculations take a long time, even days. However, the demands of actual rehabilitation are becoming increasingly rigorous, requiring that processes be completed within tens of minutes. Therefore, we developed a new effective rehabilitation system for treating patients such as those with stroke hemiplegia. The system can smoothly perform rehabilitation training on the day of admission to the hospital. We designed a heuristic BCI with simplified fuzzy reasoning, which can detect motor intention signals from an electroencephalogram (EEG) within several tens of minutes. The detected signal is sent to the newly developed ankle rehabilitation device (ARD), and the patient repeats the dorsiflexion motion by the ARD.

2A1-O05 高齢者のためのスマートデバイス連動型シニアカー

In recent years, going out opportunities for the elderly is reduced, bedridden and homebound and have been a problem. This background is the lack of movement means of the elderly. In recent years, characteristics of the elderly, is that the prevalence of smart devices, such as Internet usage and smart phones, tablet computers is increasing. In this study, we have developed a smart device linked mobility scooter in order to support the day-to-day life of the elderly with a focus on smart device. And to request the analysis of functionality required in the elderly is used in this system to implement some of these functions. Also performs fuzzy control for acceleration and deceleration of the mobility scooter with a simplified fuzzy reasoning, it is possible to suppress the acceleration applied to the elderly during traveling, and improved safety.

Design Hybrid Evolutionary PSO Aiding Miniature Aerial Vehicle Controllers

Based on some sort of simplified fuzzy reasoning methods and PID parameters, many fuzzy-PID controller schemes are applied to control the complicated systems, recently. Mathematical optimization calculations are making in encouraging EP with PSO yielding a higher-quality solution. In this paper, a novel HEPSO algorithm as an improved variant of stochastic optimization strategy PSO, which assigns optimization to fuzzy-PID control gains, is established. The benefit integration design of the HEPSO algorithm structure is generating and updating the new parameters for the fuzzy-PID control schemes in the short setting and operation time. The proposed controllers have demonstrated performance to MAV models.

The Prediction Of Air Pollution By Using Neuro-fuzzy Gmdh

Air pollution is one of today modern life phenomena. It is resulted to create round-the-clock human begin activities. Control of environment pollution is complicated scientific process that enters policy, economy, technology and sociology. GMDH (Group Method of Data Handling) has been used for the identification of a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a Neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. In this paper, is used Neuro-fuzzy GMDH algorithm for predicting air pollution data and then were compared the results of predicting air pollution data by using Neuro-fuzzy GMDH and Multi Layer Perceptron (MLP).

Numerical simulation for Fuzzy-PID controllers and helping EP reproduction with PSO hybrid algorithm

Many Fuzzy-PID controller schemes used in industry today are based on some sort of simplified fuzzy reasoning methods and PID parameters. We present a design for Fuzzy-PID controllers using a novel PSO-EP-based hybrid algorithm. We succeed in making mathematical calculations and in encouraging EP reproduction with PSO. The main advantage of our design is that the integration of the PSO-EP-based hybrid algorithm structure generates new parameters for the Fuzzy-PID control schemes. The proposed algorithm is an improved variant of PSO, a relatively recently introduced stochastic optimization strategy for Fuzzy-PID controllers that is investigated in this study. The function of Fuzzy-PID controllers is illustrated by means of a model of the induction motor control system and a higher-order numerical model.

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.

Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal

Aquatic vegetation plays a very important role in providing food, shelter, and nursery habitat and is also regarded as hydraulic resistance in the stream environment. To achieve better ecological restoration, this trade-off should be solved both hydraulically and ecologically. This study quantifies the effect of aquatic vegetation on the spatial distribution of Japanese medaka (Oryzias latipes) to evaluate its importance to fish habitat preference. The preference for aquatic vegetation index is calculated using a fuzzy preference intensity model (FPIM) with interactions among water depth, current velocity and cover ratio in an agricultural canal. In this model, simplified fuzzy reasoning is introduced to explicitly take the essential vagueness of fish behavior into consideration, and a simple genetic algorithm is used to search for an optimum model representation. Uncertainties in measurement errors and dispersions of the physical environment are positively taken into the model using symmetric triangular fuzzy numbers. To overcome the difficulty in model construction with insufficient data observed in an agricultural canal, this model was conjugated with a model developed in a laboratory experiment. The model obtained was then assessed using the AIC (Akaike’s Information Criterion) to evaluate the significance of vegetation index with a statistical approach. The results suggest the significance of vegetation index to habitat selection by Japanese medaka and that utilization of the AIC enables us to grasp the validity of an additional factor contributing to habitat prediction with a view to a definite scale

An Improved Robust Fuzzy-PID Controller With Optimal Fuzzy Reasoning

Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.

Rules Reduction of Fuzzy Reasoning Model of Multiple Division Layers by Minkowski Norm and Its Application to Modeling of Ceramic Kiln

The simplified fuzzy reasoning model having multi-layer structure has a feature that generation of fuzzy rules is easy even under actual conditions. However, it is reported that redundant fuzzy rules are generated as division layers are iteratively increased. In this paper, we propose a fuzzy reasoning model of multiple division layers, which has a weight in the reasoning output. We reduce redundant fuzzy rules by tuning up the weight using an evaluation function based on Minkowski norm. Moreover, the validity of the proposed method is verified by identification experiments of two nonlinear functions having sparse domains in teaching data. Finally, we apply the proposed method to a modeling of ceramic kiln, and confirm that it is also effective for actual problems.

Fuzzy reasoning for image compression using adaptive triangular plane patches

In this article, we present a method called adaptive patch adjustment that improves the performance of digital image data compression using triangular plane patches. The method is designed to adaptively adjust the three-dimension position of triangular plane patches that approximate the corresponding luminance curved surfaces of an original image. Such an adjustment considers the influence of all pixels contained in the projection of a patch, instead of defining a patch by only three pixels at the vertices. The influence is described and calculated with a soft computing technique, the simplified fuzzy reasoning. Experimental results show that such a flexible strategy significantly reduces the average distortion between the reconstructed image and the original one, avoiding excess block splitting, and as a result, increasing the data compression rate.

Neuro-fuzzy GMDH and its application to modelling grinding characteristics

Mathematical models, in which many input variables are involved, require a range of input and output data, since the number of parameters increases with the input variables. GMDH (Group Method of Data Handling) has been used for the identification of a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. As an example of applying the algorithm, the wheel wear equation is identified, using data from experiments of abrasive cut-off. In the model, characteristics of work materials, grinding fluids, factors of wheels, wheel velocity and table feed are used as input variables, and the grinding ratio is the resulting output. The validity of the model is confirmed within the predicted accuracy by using additional data.

A neuro-fuzzy approach to data analysis of pairwise comparisons

Artificial neural networks provide iterative on-line learning schemes for modeling non-linear systems. An iterative learning algorithm in fuzzy models, which is called neuro-fuzzy, has been recently developed within the framework of fuzzy modeling in the sense of M. Sugeno. In this paper, using neuro-fuzzy approach, two quantification methods of pairwise comparisons are presented in order to derive the associated weights of different objects. The proposed methods can be applied even in the case of incomplete pairwise comparisons. A simplified fuzzy reasoning model is obtained in the form of Gaussian radial basis functions. The psychological sensation responses of human beings to minute vibrations are analyzed by the newly proposed neuro-fuzzy approach. The proposed approach is compared with Guttman's method and Saaty's analytic hierarchy process (AHP). In our two neuro-fuzzy approaches, psychological values are obtained with the interval and the ratio scale properties. They are represented by smooth functions of class C ∞.

簡略ファジィ推論を用いたファジィモデルによる学習型制御

簡略ファジィ推論を用いたファジィモデルによる学習型制御