Sugeno-type Fuzzy System Research Articles

Development of a Hybrid Adaptive Neuro-fuzzy Inference System with Coulomb-Counting State-of-Charge Estimator for Lithium–Sulphur Battery

This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation.

Optimizing Nonlinear Parameters of Sugeno Type Fuzzy Rules using GWO for Data Classification

In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters in that dataset. Rule sets provide the knowledge base for the classification of data. Each rule set is fine-tuned using the GWO with the intention to improve the classification. The approach is compared with the work of previous researchers on similar data sets using a variety of techniques, including nature-inspired algorithms such as genetic algorithms and Swarm based algorithms. Statistical Analysis of the performance of GWO shows that it is better than five other algorithms 95% of the time.

Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network

Abrasive water jet cutting has been proven to be an effective technology for processing various engineering materials. To determine the optimal jet traverse rate (cutting speed) in abrasive water jet cutting is not very easy. This paper presents the application of an adaptive wavelet network (AWN) for overcoming this prediction problem. In this work, we consider some parameters such as change of focusing nozzle diameter, abrasive flow rate, jet pressure and depth of cut in order to control the abrasive water jet cutting process. The AWN model is adopted from an adaptable neuro-fuzzy inference system which is a Sugeno type fuzzy system that puts in the framework of this kind of systems to facilitate learning and adaptation. For model accuracy, we present to train an AWN by a hybrid learning method through a least square estimation with gradient based optimization algorithm to obtain the optimal translation and dilation parameters of the AWN. The effectiveness of the proposed approach is evaluated by the experimental data to estimate the cutting speeds for titanium and the model data is compared with the desired results. The predicted results indicated that the model can be used to identify jet traverse rates with an acceptable range of application for smooth cutting.

Evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots

An evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots is presented in this article, and issues related to the determination of the antecedent and consequent structures of the fuzzy model are addressed. In the design of the fuzzy model, determination of the type, the number of membership functions assigned to the input variables, the types of consequent equations for the fuzzy rules, the optimal number of input variables, and the dominant input variables among the input candidates are carried out using evolutionary algorithms. Using these algorithms, proper structures are evolved for the antecedent and the consequent of the Sugeno-type fuzzy model. Simulations are performed to show the effectiveness of the developed method when applied to a humanoid robot system with strong nonlinearities that have 10 input candidates.

A fuzzy convolution model for radiobiologically optimized radiotherapy margins

In this study we investigate the use of a new knowledge-based fuzzy logic technique to derive radiotherapy margins based on radiotherapy uncertainties and their radiobiological effects. The main radiotherapy uncertainties considered and used to build the model were delineation, set-up and organ motion-induced errors. The radiobiological effects of these combined errors, in terms of prostate tumour control probability and rectal normal tissue complication probability, were used to formulate the rule base and membership functions for a Sugeno type fuzzy system linking the error effect to the treatment margin. The defuzzified output was optimized by convolving it with a Gaussian convolution kernel to give a uniformly varying transfer function which was used to calculate the required treatment margins. The margin derived using the fuzzy technique showed good agreement compared to current prostate margins based on the commonly used margin formulation proposed by van Herk et al (2000 Int. J. Radiat. Oncol. Biol. Phys. 47 1121–35), and has nonlinear variation above combined errors of 5 mm standard deviation. The derived margin is on average 0.5 mm bigger than currently used margins in the region of small treatment uncertainties where margin reduction would be applicable. The new margin was applied in an intensity modulated radiotherapy prostate treatment planning example where margin reduction and a dose escalation regime were implemented, and by inducing equivalent treatment uncertainties, the resulting target and organs at risk doses were found to compare well to results obtained using currently recommended margins.

Autonomous Surgical Robot using Visual Servoing in the Extended Image Plane

This paper deals with the visibility problems occurring during the execution of a visual servoing task by a robotic scrub nurse. To deliver the instrument to the surgeon and to retrieve that instrument when the surgeon is finished using it, are the vision guide tasks to be performed by the robot. One of the main problems of a vision guide robotic scrub nurse is the lose of some image features during the control process. This problem can be produced by a temporal occlusion or an out-coming of one or more image features. When this problem appears, the common solution is simply stopped the visual servoing control task and started an initialization process. The solution proposed by the authors is to extend the image plane and compute the image features projected on it by a new prediction algorithm based on a sugeno type fuzzy system since computer vision methods can not be used because of the features are not visible. Experimental results demonstrate the improvements that can be obtained in the performance of the vision-based control task when the visual servoing in the extended image plane is used.

Fuzzy rule-base driven orthogonal approximation

In this study, orthogonal approximation concept is applied to fuzzy systems. We propose a new useful model adapted from the well-known Sugeno type fuzzy system. The proposed fuzzy model is a generalization of the zero-order Sugeno fuzzy system model. Instead of linear functions in standard Sugeno model, we use nonlinear functions in the consequent part. The nonlinear functions are selected from a trigonometric orthogonal basis. Orthogonal function parameters are trained along with the Sugeno fuzzy system. The proposed model is demonstrated using three simulations—a nonlinear piecewise-continuous scalar function modeling and filtering, nonlinear dynamic system identification, and time series prediction. Finally some performance comparisons are carried out.

A systematic fuzzy system modeling for scheduling of textile manufacturing system

This paper presents a fuzzy expert system for Textile manufacturing system using fuzzy cluster analysis. The proposed approach consists of two phases. The first phase is developed with an unsupervised learning and involves a baseline design to effectively identify a prototype fuzzy system. At this phase, a cluster analysis approach is implemented. For the aim of determination of the optimal values of clustering parameters, i.e., weighting exponent (m), and the number of clusters (c), Genetic Algorithms (GAs) are used. At the second phase, fine tuning process is done to adjust the parameters identified in the baseline design, subject to supervised learning. This phase is realized by using approximate reasoning module. Approximate reasoning parameters are also optimized, using GAs. Finally, the proposed approach is tested and validated by applying it to scheduling system of a Textile industry and comparing the results with a Sugeno-type fuzzy system modeling that uses subtractive clustering in its structure identification stage. The results show that the proposed fuzzy system has a better representation of the behavior of the complex systems, such as Textile industries.

A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation

Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.

Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization

This study considers the robust identification of the parameters describing a Sugeno type fuzzy inference system with uncertain data. The objective is to minimize the worst-case residual error using a numerically efficient algorithm. The Sugeno type fuzzy systems are linear in consequent parameters but nonlinear in antecedent parameters. The robust consequent parameters identification problem can be formulated as second-order cone programming problem. The optimal solution of this second-order cone problem can be interpreted as solution of a Tikhonov regularization problem with a special choice of regularization parameter which is optimal for robustness (Ghaoui and Lebret (1997). SAIM Journal of Matrix Analysis and Applications 18, 1035–1064). The final regularized nonlinear optimization problem allowing simultaneous identification of antecedent and consequent parameters is solved iteratively using a generalized Gauss–Newton like method. To illustrate the approach, several simulation studies on numerical examples including the modelling of a spectral data function (one-dimensional benchmark example) is provided. The proposed robust fuzzy identification scheme has been applied to approximate the physical fitness of patients with a fuzzy expert system. The identified fuzzy expert system is shown to be capable of capturing the decisions (experiences) of a medical expert.

2차 비선형 시스템에 대한 계단형 퍼지 이동 슬라이딩 평면

This note suggests a stepwise fuzzy moving sliding surface using Sugeno-type fuzzy system and presents a sliding mode control scheme using it. The fuzzy system has the angle of state error vector and the distance from the origin in the phase plane as inputs and a first-order linear differential equation as output. The surface initially passes arbitrary initial states and subsequently moves towards a predetermined surface via rotating or shifting. This method reduces the reaching and tracking time and improves robustness. Conceptually the slope of the Proposed fuzzy moving sliding surface increases stepwise in the stable region of the phase plane. The surface, however, rotates continuously because the surface is a fuzzy system. The asymptotic stability of the fuzzy sliding surface is proved. The validity of the proposed control scheme is shown in computer simulation for a second-order nonlinear system.