The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their work...

In today’s life Intrusion Detection System gain the attention, because of ability to detect the intrusion access efficiently and effectively as security is the major issue in networks. This system identifies attacks and reacts by generating alerts or blocking the unwanted data/traffic. Intrusion Detection System mainly classified as Anomaly based intrusion detection systems that have benefit of...

The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of ...

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that indi...

the error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. global positioning system is the best aiding system in this case. in this paper we first simulate a gps and ins; then simulate tightly integration and finally review adaptation method of kalman filtering a fuzzy adaptive kalma...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

In previous work, we have described methods that we have developed for tuning a fuzzy data mining system for intrusion detection using a hierarchical genetic algorithm. Unfortunately, the genetic algorithm approach is very slow due to the computational cost of the evaluation function. In this paper, we describe parallel implementations of the genetic algorithm that were run on both a multiproce...

In view of the shortage of e-insensitive loss function for Gaussian noise, this paper presents a new version of fuzzy support vector machine (SVM) which can penalize Gaussian noise to forecast fuzzy nonlinear system. Since there exist some problems of finite samples and uncertain data in many forecasting problem, the input variables are described as crisp numbers by fuzzy comprehensive evaluati...

The experts considered in this paper are neural networks whose forecasts are combined by another neural network, a gate. For regression problems such an architecture was shown to partly remedy the two main problems in forecasting real world time series: nonstationarity and overfitting. The goal of this paper is to compare the forecasting ability of gated experts (GE) with a that of a single neu...

This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through divers...