This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFN (MEPGAN). The propos...

Now a day’s software cost/effort estimation is a very complex job to do. Several estimation techniques have been developed in this regard. This assessment of parameters like, time, cost, and number of staff required sequentially which in turn is to be done at an early stage. Constructive Cost model which is also known as COCOMO model was one of the best model to estimate the cost and time in pe...

Clustering techniques have always been oriented to solve classification and pattern recognition problems. This clustering techniques have been used also to initialize the centers of the Radial Basis Function (RBF) when designing an RBF Neural Network (RBFNN) that approximates a function. Since classification and function approximation problems are quite different, it is necessary to design a ne...

In this paper, a hybrid network based on the combination of Radial Basis Function Networks (RBFNs) and Gaussian Mixture Models (GMMs) is proposed and used for speaker recognition. The hybrid network is a hierarchical one, where a GMM is built for each speaker and an RBFN is built for each group of speakers. The GMMs and RBFNs are trained independently. The RBFNs are used as a rst stage coarse c...

This paper shows how scale-based clustering can be done using the radial basis function network (RBFN), with the RBF width as the scale parameter and a dummy target as the desired output. The technique suggests the "right" scale at which the given data set should be clustered, thereby providing a solution to the problem of determining the number of RBF units and the widths required to get a goo...

Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based l...

This paper presents a comparative study of different neural network models for forecasting the weather of Vancouver, British Columbia, Canada. For developing the models, we used one year’s data comprising of daily maximum and minimum temperature, and wind-speed. We used Multi-Layered Perceptron (MLP) and an Elman Recurrent Neural Network (ERNN), which were trained using the one-step-secant and ...

Resumen. En este artículo se presenta una metodología hibrida basada en un modelo difuso del tipo 1 en versión singleton usando un diseño factorial 2 que optimiza el modelo del sistema experto y sirve para realizar inspección en línea. El método de diseño factorial proporciona la base de datos necesaria para realizar la creación de la base de reglas para el modelo difuso y también genera la bas...

The existing Chinese Minorities OCR system is mainly oriented in the "literacy" level, the script recognition has not attracted the attention it deserves, and the area of recognizing the kinds of Chinese minority scripts is still in a blank. The method of recognizing the kinds of Chinese minority scripts based on wavelet analysis and Radial Basis Function Network (RBFN) is presented which adopt...

Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators but achieve faster convergence and are capable of dealing with the so-called “curse of dimensionality.” In addition, WNN are generalized RBFN. However, the generalization performance of WNN trained by least-squares appro...