Extracting previously unknown patterns from huge volume of data is the primary objective of any data mining algorithm. In recent days there is a tremendous growth in data collection due to the advancement in the field of information technology. The patterns revealed by data mining algorithm can be used in various domains like Image Analysis, Marketing and weather forecasting. As a side effect o...

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the tested on NASDAQ stock market indices which among most widely followed United States. Empirical results show that by determining parameters ANFIS (premise and consequent parameters) using GA, we can improve performance terms Mean...

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and ...

In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values. There is a growing awareness of the need for database systems to be able to handle and correctly process data with uncertainty. The unce...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

Based on the problems caused by today conventional vehicles, much attention has been put on the fuel cell vehicles researches. However, using a fuel cell system is not adequate alone in transportation applications, because the load power profile includes transient that is not compatible with the fuel cell dynamic. To resolve this problem, hybridization of the fuel cell and energy storage device...

stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory but not accurate. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. A multi agent framework is proposed for the implementation of the system. Ex...

Nowadays, forecasting and techniques used to obtain the forecasts are very important. The term of forecast means to make an inference (predict) about the future on the basis of existing information. Especially, forecasting of stock market data are frequently used in time series analysis literature. Moreover, fuzzy time series forecasting methods have been widely used in the analysis of stock ma...

A fuzzy logic algorithm has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise. Genetic algorithm based optimization is conducted to determine the form of the membershi...

This paper presents the development of a hybrid neural network to model a fuzzy expert system for time series forecasting of electricc load. The hybrid neural network is trained to develop fuzzy logic rules andjind optimal inputloutput membership values of load and weather parameters. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the ...