There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric models [3], MLP [4]). The main motivation of these methods is to benefit from the enforced inner structure of the data. This paper presents how functional data can be used with RBFN, and how the inner structure of the form...

The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist...

This paper presents a supervising learning approach using Multilayer Feed Forward Neural Network(MFFN) and Radial Basis Fuction Neural Network(RBFN) to deal with fast and accurate static security assessment (SSA) and contingency analysis of a large electric power systems. The degree of severity of contingencies is measured by two scalar performance indices (PIs): Voltage-reactive power performa...

Time series forecasting is an important tool, which is used to support the areas of planning for both individual and organizational decisions. This problem consists of forecasting future data based on past and/or present data. This paper deals with the problem of time series forecasting from a given set of input/output data. We present a hybrid approach for time series forecasting using Radial ...

In World Wide Web environments, recommender systems are useful to reduce information overloading. A content-based recommender system recommends items according to their features. Vector Space Model (VSM) is a popular way to recommend items that are similar to those the user liked in the past. The main disadvantages of this content-based method are overspecialization and new user problems that i...

Nowadays, many real applications comprise data-sets where the distribution of the classes is significantly different. These data-sets are commonly known as imbalanced data-sets. Traditional classifiers are not able to deal with these kinds of data-sets because they tend to classify only majority classes, obtaining poor results for minority classes. The approaches that have been proposed to addr...

A self-organizing fuzzy controller (SOFC) has been developed for control engineering applications. However, in practical applications, it is difficult to choose the values of the SOFC’s learning rate and weighting distribution appropriately to achieve reasonable control performance. In addition, the SOFC is mainly used to control singleinput single-output systems. When the SOFC is applied to ma...

This paper deals with the problem of function approximation from a given set of input/output data. This paper presents a new approach for solving the problem of function approximation from a given set of I/O data using Wavelet Neural Networks (WNN) and Genetic Algorithms (GAs). GAs has the property of global optimal search algorithm and WNNs are universal approximations, it’s achieved faster co...

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. ...

A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed in this paper. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attach these problems, neural networks have become popular for DOA estimation in recent years. However, ...