The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water ...

Forecasting currency exchange rates are an important financial problem that is receiving increasing attention especially because of its intrinsic difficulty and practical applications. This paper presents improved neural network and fuzzy models used for exchange rate prediction. Several approaches including multi-layer perceprtons, radial basis functions, dynamic neural networks and neuro-fuzz...

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the ...

This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits. Both techniques are based on the simulation before test approach: a “fault dictionary” is a priori generated by collecting signatures of different fault conditions. Classifiers, trained by the examples contained in the fault dictionary, are then configured to classify the measured cir...

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...

A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integralderivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. Th...

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. In...

In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the number of basis functions and smoothing par...

We propose a learning approach to designing fuzzy controllers based on the B-spline model. Unlike other normalised parameterised set functions for deening fuzzy sets, B-spline basis functions do not necessarily span from membership values zero to one, but possess the property \partition of unity". B-spline basis functions can be automatically determined after each input is partitioned. Learning...

This paper considers the testing of fuzzy hypotheses on the basis of a Bayesian approach. For this, using a notion of prior distribution with interval or fuzzy-valued parameters, we extend a concept of posterior probability of a fuzzy hypothesis. Some of its properties are also put into investigation. The feasibility and effectiveness of the proposed methods are also cla...