In this paper we introduce the notion of weak and strong intuitionistic fuzzy (Schauder) basis on an intuitionistic fuzzy n-normed linear space [5] and prove that an intuitionistic fuzzy n-normed linear space having a weak intuitionistic fuzzy basis is separable. Also we discuss approximation property on the same space. Mathematics Subject Classification: 03B20, 03B52, 46A99, 46H25

In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function ...

in this study, we introduce and study a concept of distributed fuzzymodeling. fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. in contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. i...

This paper describes Weighted Radial Basis Functions, a neuro-fuzzy uni cation algorithm which mixes Perceptrons and Radial Basis Functions. The algorithm has been tested as a pattern classi er in practical applications. Its performance are compared against those of other neural classi ers. The proposed algorithm has performance comparable or better than other neural algorithms, although it can...

In this paper we propose a novel training algorithm for RBF networks that is based on extended kalman filter and fuzzy logic.After the user choose how many prototypes to include in the network, the extended kalman filter simultancously solves for the prototype vectors and the weight matrix.The fuzzy logic is used to cope with the devergence problem caused by the insufficiently known a priori fi...

In this paper a numerical technique is presented for the solution of fuzzy linear Volterra-Fredholm-Hammerstein integral equations. This method is a combination of collocation method and radial basis functions(RBFs).We first solve the actual set are equivalent to the fuzzy set, then answer 1-cut into the equation. Also high convergence rates and good accuracy are obtain with the propose method ...

In this paper, we present a new fuzzy reasoning method based on the compensating fuzzy reasoning (CFR). Its basicidea is to obtain a new fuzzy reasoning result by moving and deforming the consequent fuzzy set on the basis of themoving, deformation, and moving-deformation operations between the antecedent fuzzy set and observation information.Experimental results on real-world data sets show tha...

This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the autotuning method of learning rate and momentum is proposed and applied...

Radial fhndions (RBF) network is an important common place in neum-fnzzy systems. In particolar, their common dversal approximator properties make hrzzy systems as well as n e o d network systems excellent representations for system modelling. In data-based modelling, it is important that, the overfitting should be avoided to eohana the generslisstion cspabaty of the model since tbh is BII olti...