In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic weights, but also the membership functions parameters. The proposed algorithm has both the tracking and filtering properties, so the NFN can be effectively used for prediction, filtering, and restoration of non-statio...

In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class...

In order to make up some deficiencies of the fuzzy c-means clustering algorithm, a new FCM algorithm based on pretreatment of similarity relation between samples is proposed in the paper, which is utilized to estimate the fuzzy clustering centers and the weight coefficient of samples effecting on the fuzzy clustering centers during iteration process. The new FCM algorithm makes the clustering q...

The extraction of easily interpretable knowledge from the large amount of data measured in experiments is well desirable. This paper proposes a method to achieve this. A fuzzy rule system isjirst generated and optimized using evolution strategies. This fuzzy system is then converted to an RBF neural network to reJine the obtained knowledge. In order to extract understandable fuzzy rules from th...

The objective of this paper is to establish a class of models for solving the weight problem in fuzzy logic. First, some constraints that weighted fuzzy logic should satisfy are given based on Triangular norms and conorms. Then, based on these constraints, a class of models (referred to as relative weighted models) are established for handling weights in fuzzy logic. These models are novel in t...

This study describes a distributed attack detection approach for a tactical MANET using intelligent agents equipped with inference systems based on fuzzy logic. The results produce a prototype intrusion detection system capable of effectively detecting attacks in a tactical MANET with accuracy approaching 95%. The attack recognition system is implemented using stationary intelligent fuzzy agent...

Relational clustering is an extension of clustering for relational data. Fuzzy c-Medoids (FCMdd) based linear fuzzy clustering extracts intrinsic local linear substructures from relational data. However this linear clustering was affected by noise or outliers because of using Euclidean distance. Alternative Fuzzy c-Means (AFCM) is an extension of Fuzzy c-means, in which a modified distance meas...

The presence or absence of a BWER within a storm is important for severe weather forecasting. The three dimensional structure of a BWER is often not clearly deened in radar data and a fuzzy logic approach where the various uncertainties associated with a BWER's radar proole (capping, three-dimensional structure, vertical height, reeectivity ranges, proximity to a mesocyclone, etc.) are taken in...

Abstract: In many power factor correction applications, the power level can reach maximum value and sometimes the input voltage also will be too high. For high power and high voltage applications the inductor volume and weight is the major concern of the conventional boost converter. In this paper the two inductor boost converter is adopted which uses smaller inductors yielding high power densi...

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neura...