The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...

Klaus S hmid and Volker Krebs Universität Karlsruhe (TH), Institut für Regelungsund Steuerungssysteme Kaiserstr. 12, D-76131 Karlsruhe, Germany e-mail: {s hmid, krebs} irs.ete .uni-karlsruhe.de Abstra t. A dynami fuzzy system is a mapping of fuzzy input values onto a fuzzy output value with a feedba k to the input. In this paper, we present a new rule-based inferen e method that an be used in d...

This study presents a new method for modeling an adaptive neuro-fuzzy inference system (ANFIS) based on vibration for predicting surface roughness in the CNC turning process. The input parameters of the model are insert nose radius, cutting speed, feed rate, depth of cut and vibration amplitude, which determine the output parameter of the surface roughness. A Gauss type membership function was ...

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 o...

This paper presents a development of a self-tuning fuzzy PID controller to overcome the appearance of nonlinearities and uncertainties in the system. The self-tuning fuzzy PID controller is the combination of a classical PID and fuzzy controller. The controller is designed based on the expert knowledge of the system. Fuzzy logic is used to tune each parameter of PID controller. Appropriate fuzz...

In recent years, fuzzy modelling has become very popular because of its ability to assign meaningful linguistic labels to fuzzy sets in the rule base. However, in order to achieve better performance in fuzzy modelling, parameter identification often needs to be performed. In this paper, we address this optimization problem using memetic algorithms (MAs) for Sugeno and Yasukawa's (SY) qualitativ...

In this paper, a clustering-based method is proposed for automatically constructing a multi-input TakagiSugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to prov...

Article history: Received 31 July 2005 Received in revised form 27 September 2005 Accepted 5 October 2005 The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an ...

Several algorithms exist to address the issues concerning parameter reduction of soft sets. The most recent concept of Normal Parameter Reduction (NPR) is introduced, which overcomes the problem of suboptimal choice and added parameter set of soft sets. However, the algorithm involves a great amount of computation. In this thesis, a New Efficient Normal Parameter Reduction algorithm (NENPR) of ...